text:
\section{Preprocessing} \label{preprocessing}

\begin{itemize}
  \item \textbf{Type I:} No preprocessing
  \item \textbf{Type II:}
  Emotion icons were converted to their string text using the "emoji" Python library. Then, URLs were converted to  "HTTPURL" token, also every mention in a tweet was converted to "@USER" token using regular expressions. These conversions was done because the BERTweet model was pre-trained on tweets after these conversions.
  \item \textbf{Type III:}
  Same as in Type-III besides converting the smiley face codes e.g. ":‑)" and ":)" to one of three values (smiley, sad, and playful). More than two successive occurrences of any punctuation like in "why?!!!!" were removed, then we removed more than two successive occurrences of same character like in "Superrrr" which can be found frequently in tweets. Moreover, a contraction (e.g. "isn't and "'cause") was replaced with its full form (e.g. "is not" and "because").
  \item \textbf{Type IV:}
  Same as in Type-III besides stemming and stop-word removal. For English we used WordNet lemmatizer and for Arabic we used ISRI stemmer, all from the NLTK Python library\footnote{\href{https://www.nltk.org/}{NLTK Python Library}}.
\end{itemize}

\section{Appendix}
\label{sec:appendix}

\subsection{Hierarchical Architecture}
\label{Hier_Network}
Figure \ref{fig:Hierarchical_Network} shows a hierarchical network based on a transformer. The input tokens are passed to the transformer (BERTweet), then the output token embeddings are passed to a Bi-LSTM layer which can be with or without attention mechanism.
\begin{figure}[h]
\centering
\includegraphics[width=\linewidth ]{images/hier_arch2.pdf}
\caption{Hierarchical architecture}
\label{fig:Hierarchical_Network}
\end{figure}

\subsection{Sarcasm Types Description}
\label{ssec:Sarcasm-types-description}
    \begin{enumerate}
        \item \textbf{Sarcasm:} tweets that contradict the state of affairs and are critical towards an addressee.

        \item \textbf{Irony:} tweets that contradict the state of affairs but are not obviously critical towards an addressee.

        \item \textbf{Satire:} tweets that appear to support an addressee, but contain underlying disagreement and mocking.

        \item \textbf{Understatement:} tweets that undermine the importance of the state of affairs they refer to.

        \item \textbf{Overstatement:} tweets that describe the state of affairs in obviously exaggerated terms.

        \item \textbf{Rhetorical question:} tweets that include a question whose invited inference (implicature) is obviously contradicting the state of affairs.
    \end{enumerate}

\subsection{BERT Classification Architecture}
\label{BERT-arch}

Figure \ref{fig:BERT_classification_arch} shows the BERT-base classification architecture\footnote{\href{https://mccormickml.com/2019/07/22/BERT-fine-tuning/}{BERT Fine-Tuning Tutorial with PyTorch} by Chris McCormick and Nick Ryan}.
From the output of the final (12th) transformer, only the first embedding (corresponding to the [CLS] token) is used by a classifier.

\begin{figure}[h]
\centering
\includegraphics[width=\linewidth]{images/BERT_classification_arch.pdf}
\caption{BERT classification architecture}
\label{fig:BERT_classification_arch}
\end{figure}

\subsection{ELECTRA:- Replaced Token Detection}
\label{ELECTRA-arch}
ELECTRA (\textbf{E}fficiently \textbf{Le}arning an \textbf{E}ncoder that \textbf{C}lassifies \textbf{T}oken \textbf{R}eplacement \textbf{A}ccurately) \cite{clark2020electra} replaces the MLM of BERT with Replaced Token Detection (RTD), which looks to be more efficient and produces better results. In BERT, the input is replaced by some tokens with [MASK] and then a model is trained to reconstruct the original tokens.

In ELECTRA, instead of masking the input, the approach corrupts it by replacing some input tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original tokens, a discriminative model is trained that predicts whether each token in the corrupted input was replaced by a generator sample or not.

This approach trains two neural networks, a generator and a discriminator. Each one primarily consists of an encoder (e.g., a transformer network) that maps a sequence of input tokens into a sequence of contextualized vector representations. The discriminator then predicts whether it's fake by analyzing its data distribution. An overview of replaced token detection is shown in Figure \ref{fig:ELECTRA_arch}.

\begin{figure*}[]
\centering
\includegraphics[width=0.8\textwidth]{images/ELECTRA.pdf}
\caption{An overview of replaced token detection}
\label{fig:ELECTRA_arch}
\end{figure*}

\subsection{Effect of Learning Rates and Epochs in Subtask A (English)}
\label{Learning_Rates_Epochs}
In Subtask A (English), we experimented the BERT model on the B4 dataset with different learning rates and epochs as shown in Table \ref{tab:TaskA-En-Learning-Rates} and Table \ref{tab:TaskA-En-Epochs} respectively. As shown in the tables the best learning rate was 4e-6 and the best number of epochs was 5.

\begin{table}[h]
\centering
\begin{tabular}{|c|c|c|}
\hline
\rowcolor[HTML]{739CB9}
{\color[HTML]{FFFFFF} \textbf{Val}} & {\color[HTML]{FFFFFF} \textbf{Test}} & {\color[HTML]{FFFFFF} \textbf{Learning Rate}} \\ \hline
\rowcolor[HTML]{FFFFFF}
0.4002 & 0.25   & 1 e - 5 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.5871 & 0.4558 & 9 e - 6 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.5913 & 0.4639 & 8 e - 6 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.4002 & 0.25   & 7 e - 6 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.4002 & 0.25   & 6 e - 6 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.5952 & 0.4883 & 5 e - 6 \\ \hline
\cellcolor[HTML]{D5AB6D}0.6025      & \cellcolor[HTML]{6D9EEB}0.4769       & \cellcolor[HTML]{FFFFFF}4 e - 6              \\ \hline
\rowcolor[HTML]{FFFFFF}
0.5798 & 0.475  & 3 e - 6 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.5644 & 0.4724 & 2 e - 6 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.6012 & 0.5017 & 1 e - 6 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.4002 & 0.25   & 9 e - 7 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.4002 & 0.25   & 8 e - 7 \\ \hline
\end{tabular}
\caption{The effect of learning rates on performance of the BERT model on the B4 dataset with weighted loss function}
\label{tab:TaskA-En-Learning-Rates}
\end{table}

\begin{table}[h]
\centering
\begin{tabular}{|c|c|c|}
\hline
\rowcolor[HTML]{739CB9}
{\color[HTML]{FFFFFF} \textbf{Val}} & {\color[HTML]{FFFFFF} \textbf{Test}} & {\color[HTML]{FFFFFF} \textbf{Epochs}} \\ \hline
\rowcolor[HTML]{FFFFFF}
0.5282 & 0.5304 & 1  \\ \hline
\rowcolor[HTML]{FFFFFF}
0.5877 & 0.5092 & 3  \\ \hline
\cellcolor[HTML]{D5AB6D}0.6025 & \cellcolor[HTML]{6D9EEB}0.4769 & \cellcolor[HTML]{FFFFFF}5     \\ \hline
\rowcolor[HTML]{FFFFFF}
0.6019 & 0.4647 & 7  \\ \hline
\rowcolor[HTML]{FFFFFF}
0.5856 & 0.457  & 10 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.6017 & 0.5099 & 13 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.594  & 0.475  & 15 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.575  & 0.4943 & 17 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.5882 & 0.4675 & 20 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.5938 & 0.4633 & 23 \\ \hline
\rowcolor[HTML]{FFFFFF}
0.575  & 0.4926 & 25 \\ \hline
\end{tabular}
\caption{The effect of epochs on performance of the BERT model on the B4 dataset with weighted loss function}
\label{tab:TaskA-En-Epochs}
\end{table}

\subsection{Performance of BERT+BiLSTM with and without Attention in Subtask A (English)}
\label{BERT-BiLSTM-appendix}

\textbf{BERT+BiLSTM:}
The best results we got using this architecture were on the B4 dataset with 4e-6 learning rate, 10 epochs, and 50 LSTM hidden state size. Table \ref{tab:TaskA-En-BERT-RNN} shows the results using same hyperparameters but with different hidden state sizes.

\begin{table}[h]
\centering
\begin{tabular}{|c|c|c|}
\hline
\rowcolor[HTML]{739CB9}
{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Hidden \\ State Size\end{tabular}}} & {\color[HTML]{FFFFFF} \textbf{Val}} & {\color[HTML]{FFFFFF} \textbf{Test}} \\ \hline
50  & \cellcolor[HTML]{D5AB6D}0.6027 & 0.4741 \\ \hline
100 & 0.5882                         & 0.4765 \\ \hline
300 & 0.5813                         & 0.4627 \\ \hline
600 & 0.561                          & 0.4702 \\ \hline
900 & 0.5831                         & 0.4516 \\ \hline
\end{tabular}
\caption{Results of the BERT+BiLSTM model for SubTask A (English) on the B4 dataset}
\label{tab:TaskA-En-BERT-RNN}
\end{table}

\textbf{BERT+BiLSTM+Attention:}
The best results we got using this architecture were on the B3 dataset with 4e-6 learning rate, 5 epochs, and 600 LSTM hidden
state size. Table \ref{tab:TaskA-En-BERT-RNN-ATT} shows the results using same hyperparameters but with different hidden state sizes.

\begin{table}[h]
\centering
\begin{tabular}{|c|c|c|}
\hline
\rowcolor[HTML]{739CB9}
{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Hidden State \\ Size\end{tabular}}} & {\color[HTML]{FFFFFF} \textbf{Val}} & {\color[HTML]{FFFFFF} \textbf{Test}} \\ \hline
50  & 0.5726                         & 0.4685 \\ \hline
100 & 0.5978                         & 0.468  \\ \hline
300 & 0.5935                         & 0.4627 \\ \hline
600 & \cellcolor[HTML]{D5AB6D}0.6087 & 0.4625 \\ \hline
900 & 0.5777                         & 0.4618 \\ \hline
\end{tabular}
\caption{Results of the BERT+BiLSTM+Attention model for SubTask A (English) on B3 dataset}
\label{tab:TaskA-En-BERT-RNN-ATT}
\end{table}

\subsection{More Information about the Datasets:}
\label{More_Information}
Density of the number of words in tweets and their rephrases in the original datasets is shown in Figure \ref{fig:OriginalEnglishNumberOFWords} for English and in Figure \ref{fig:OriginalArabicNumberOFWords} for Arabic.

\begin{figure}[h]
\centering
\includegraphics[width=\columnwidth]{images/OriginalEnglishNumberOFWords.pdf}
\caption{Density of the number of words in the original English training (top) and testing (bottom) datasets.}
\label{fig:OriginalEnglishNumberOFWords}
\end{figure}

\begin{figure}[h]
\centering
\includegraphics[width=\columnwidth]{images/OriginalArabicNumberOFWords.pdf}
\caption{Density of the number of words in the original Arabic training (top) and testing (bottom) datasets.}
\label{fig:OriginalArabicNumberOFWords}
\end{figure}

In addition to this, information about dialects for Arabic subtasks is presented in Figure \ref{fig:OriginalArabicDialects} and Table \ref{tab:Dialects}.

\begin{figure}[h]
\centering
\includegraphics[width=\columnwidth]{images/OriginalArabicDialects.pdf}
\caption{Percentage of dialects of tweets in the original Arabic training (top) and testing (bottom) datasets.}
\label{fig:OriginalArabicDialects}
\end{figure}

\begin{table}[h]
\begin{tabular}{|c|c|c|c|c|}
\hline
\rowcolor[HTML]{739CB9}
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  {\color[HTML]{FFFFFF} \textbf{Eygptian}} &
  {\color[HTML]{FFFFFF} \textbf{MSA}} \\ \cline{4-5}
\rowcolor[HTML]{739CB9}
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  {\color[HTML]{FFFFFF} \textbf{Levantine}} &
  {\color[HTML]{FFFFFF} \textbf{Gulf}} \\ \cline{4-5}
\rowcolor[HTML]{739CB9}
\multirow{-3}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}}} &
  \multirow{-3}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Split}}} &
  \multirow{-3}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}}} &
  {\color[HTML]{FFFFFF} \textbf{Maghrebi}} &
  {\color[HTML]{FFFFFF} \textbf{}} \\ \hline
 &
  \cellcolor[HTML]{FFFFFF} &
   &
  41.7 &
  49 \\ \cline{4-5}
 &
  \cellcolor[HTML]{FFFFFF} &
   &
  3.7 &
  2.7 \\ \cline{4-5}
 &
  \multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}Train} &
  \multirow{-3}{*}{3102} &
  2.9 &
   \\ \cline{2-5}
 &
  \cellcolor[HTML]{FFFFFF} &
   &
  37.1 &
  34.4 \\ \cline{4-5}
 &
  \cellcolor[HTML]{FFFFFF} &
   &
  12 &
  12.6 \\ \cline{4-5}
\multirow{-6}{*}{Original} &
  \multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}Test} &
  \multirow{-3}{*}{1400} &
  3.9 &
   \\ \hline
 &
  \cellcolor[HTML]{FFFFFF} &
   &
  21.2 &
  68.2 \\ \cline{4-5}
 &
  \cellcolor[HTML]{FFFFFF} &
   &
  5 &
  5.1 \\ \cline{4-5}
 &
  \multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}Train} &
  \multirow{-3}{*}{12548} &
  0.3 &
   \\ \cline{2-5}
 &
  \cellcolor[HTML]{FFFFFF} &
   &
  10.2 &
  77.4 \\ \cline{4-5}
 &
  \cellcolor[HTML]{FFFFFF} &
   &
  1.6 &
  10.7 \\ \cline{4-5}
\multirow{-6}{*}{\begin{tabular}[c]{@{}c@{}}ArSarcasm\\ -v2\end{tabular}} &
  \multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}Test} &
  \multirow{-3}{*}{3000} &
  0.1 &
   \\ \hline
\end{tabular}
\caption{Distribution of tweets over dialects in Arabic Datasets}
\label{tab:Dialects}
\end{table}

Information about the distribution of sarcastic and non-sarcastic tweets in the original datasets is presented in Figure \ref{fig:OriginalEnglishArabicTaskAC}.

\begin{figure}[h]
\centering
\includegraphics[width=\columnwidth]{images/OriginalEnglishArabicTaskAC.pdf}
\caption{Percentage of sarcastic and non-sarcastic tweets in the original English and Arabic training (top) and testing (bottom) datasets.}
\label{fig:OriginalEnglishArabicTaskAC}
\end{figure}

Information about sarcastic labels, for subTask B, in the original datasets is shown in Figure \ref{fig:OriginalEnglishTaskB}.

\begin{figure}[h]
\centering
\includegraphics[width=\columnwidth]{images/OriginalEnglishTaskB.pdf}
\caption{Percentage of tweets under each sarcastic label in the original English training (top) and testing (bottom) datasets.}
\label{fig:OriginalEnglishTaskB}
\end{figure}
\section{Conclusion and Future Direction}

We built our systems using stand-alone transformer based models and hierarchical ones by adding a BiLSTM layer with or without attention mechanism. In addition to this, we created new datasets depending on the subtask using a combination of original and external datasets besides augmenting using word embedding or repetition. Our results shows that the augmented datasets enhanced the results for all subtasks. Moreover, we found that the fine-tuned stand-alone transformer based models in each subtask gave the best results especially with Type-II preprocessing. We also showed the enhancement when using a weighted loss function and the effect of using different learning-rates, epochs, and preprocessing types. Moreover, the rank of our team was consistent across most of the subtasks (the fourth rank) which shows the robustness of the techniques we used.
\section{Abbreviations} \label{Abbreviations}
The following abbreviations were used frequently, and are shown in Table  \ref{tab:Abbreviations}.

\begin{table}[h]
\centering
\begin{tabular}{|c|c|}
\hline
\rowcolor[HTML]{739CB9}
{\color[HTML]{FFFFFF} \textbf{Full Form}} & {\color[HTML]{FFFFFF} \textbf{Abbreviation}} \\ \hline
\cellcolor[HTML]{FFFFFF}Language  & Lang \\ \hline
\cellcolor[HTML]{FFFFFF}Sarcastic & S    \\ \hline
Non-Sarcastic                     & NS   \\ \hline
English                           & En   \\ \hline
\cellcolor[HTML]{FFFFFF}Arabic    & Ar   \\ \hline
\cellcolor[HTML]{FFFFFF}External    & Ext   \\ \hline
\end{tabular}
\caption{Abbreviations used in the paper}
\label{tab:Abbreviations}
\end{table}

\section{Dataset} \label{dataset}

The organizers provided datasets for English and Arabic. Regardless of the dataset, these are the fields in each row of a dataset:
\begin{itemize}
    \item \textbf{Tweet}: A text specifying a tweet. This field is for all subtasks.
    \item \textbf{Sarcastic}: A binary field specifying whether a tweet is sarcastic or not. This field is for subtask A.
    \item \textbf{Rephrase}: A text specifying a non-sarcastic rephrase of a sarcastic tweet. This field is for subtask C.
    \item \textbf{Sarcasm, Irony, Satire, Understatement, Overstatement, and Rhetorical question} (English only): These binary fields are the labels of a sarcastic tweet. These fields are for subtask B.
    \item \textbf{Dialect} (Arabic only): A text specifying the dialect of a tweet from one of five dialects: Modern Standard Arabic (MSA), Egyptian, Levantine, Maghrebi, and Gulf. This field is for subtask A and C.
\end{itemize}

In the following sections we will describe the datasets given by the organizers, other available datasets, and augmented datasets.

\subsection{Datasets given by organisers (Original)}
The organizers released two training datasets for Arabic and English to train the systems on them for all subtasks. Later on, they released one testing dataset for each subtask. We called these datasets "original" datasets as they are the official datasets for the subtasks. Information about the distribution of sarcastic and non-sarcastic tweets is presented in Table \ref{tab:TaskA-Datasets} and in the appendix \ref{More_Information} - Figure \ref{fig:OriginalEnglishArabicTaskAC}. Furthermore, information about sarcastic labels for subtask B is presented in Table \ref{tab:TaskB-Datasets} and in the appendix \ref{More_Information} - Figure \ref{fig:OriginalEnglishTaskB}.

\subsection{Other Available Datasets (External)}
The datasets in this section are not the official datasets and thus we called them "external" datasets. However, we used these datasets for subtask A and subtask B as they are created for similar subtasks.

\textbf{Datasets Downloaded using Twitter API:}
Initially the organizers provided the participants with a training and testing datasets which covered subtask A and subtask B for English. However, they provided the tweet ID instead of the tweet text and they asked the participants to download the tweet text using the tweet ID via Twitter API\footnote{\href{https://developer.twitter.com/en/docs/api-reference-index}{developer.twitter.com/en/docs/api-reference-index}}. Therefore, we downloaded some of the tweet texts for these two datasets and other tweet texts we could not find. We were able to download 2841 tweets for training and 713 tweets for testing as shown in Table \ref{tab:TaskA-Datasets}. The distribution of the tweets over the sarcastic labels is presented in Table \ref{tab:TaskB-Datasets}.

\textbf{Datasets of SemEval-2018 Task 3:}
These datasets are on same subtasks of subtask A and subtask B but for SemEval 2018 \citep{VanHee2018}. We used the dataset for subtask A which has emojis and sarcasm hashtags. The datasets are available for download in this link\footnote{\href{https://github.com/Cyvhee/SemEval2018-Task3/tree/master/datasets/train}{SemEval2018-Task3 Dataset}}. More information about the distribution of sarcastic and non-sarcastic tweets is presented in Table \ref{tab:TaskA-Datasets}.

\textbf{ArSarcasm-v2 Dataset:}
It contains a training and testing datasets for sarcasm detection in Arabic \citep{abu-farha-etal-2021-overview}. Each row contains a tweet, sarcastic class, sentiment, and dialect. The datasets are available for download in this link\footnote{\href{https://github.com/iabufarha/ArSarcasm-v2}{ArSarcasm-v2}}. More information about the distribution of sarcastic and non-sarcastic tweets is presented in Table \ref{tab:TaskA-Datasets}.

\begin{table}[h]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{|l|
>{\columncolor[HTML]{FFFFFF}}l |l|l|l|l|}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Split}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Lang}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{S\%}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{NS\%}} \\ \hline
Original     & Train & En                         & 3468  & 25   & 75   \\ \hline
Original     & Train & Ar                         & 3102  & 24   & 76   \\ \hline
Original     & Test  & En                         & 1400  & 14.3 & 85.7 \\ \hline
Original     & Test  & Ar                         & 1400  & 14.3 & 85.7 \\ \hline
Twitter API  & Train & En                         & 2841  & 16.8 & 83.2 \\ \hline
Twitter API  & Test  & En                         & 713   & 16.8 & 83.2 \\ \hline
SemEval 2018 & Train & \cellcolor[HTML]{FFFFFF}En & 3834  & 49.8 & 50.2 \\ \hline
ArSarcasm-v2 & Train & \cellcolor[HTML]{FFFFFF}Ar & 12548 & 17.3 & 82.7 \\ \hline
ArSarcasm-v2 & Test  & Ar                         & 3000  & 27.4 & 72.6 \\ \hline
\end{tabular}
}
\caption{Total number of tweets and percentage of sarcastic and non-sarcastic tweets in each dataset for subtask A.}
\label{tab:TaskA-Datasets}
\end{table}

\begin{table}[h]
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\rowcolor[HTML]{739CB9}
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  {\color[HTML]{FFFFFF} \textbf{Sarcasm\%}} &
  {\color[HTML]{FFFFFF} \textbf{Irony\%}} &
  {\color[HTML]{FFFFFF} \textbf{Satire\%}} \\ \cline{4-6}
\rowcolor[HTML]{739CB9}
\multirow{-2}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}}} &
  \multirow{-2}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Split}}} &
  \multirow{-2}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}}} &
  {\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Under-\\ statement\%\end{tabular}}} &
  {\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Over-\\ statement\%\end{tabular}}} &
  {\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Rhetorical\\ question\%\end{tabular}}} \\ \hline
                                                                         & \cellcolor[HTML]{FFFFFF}                        &                        & 68.3 & 14.8 & 2.4  \\ \cline{4-6}
                                                                         & \multirow{-2}{*}{\cellcolor[HTML]{FFFFFF}Train} & \multirow{-2}{*}{3468} & 1    & 3.8  & 9.7  \\ \cline{2-6}
                                                                         & \cellcolor[HTML]{FFFFFF}                        &                        & 66.4 & 7.4  & 18.1 \\ \cline{4-6}
\multirow{-4}{*}{Original}                                               & \multirow{-2}{*}{\cellcolor[HTML]{FFFFFF}Test}  & \multirow{-2}{*}{1400} & 0.4  & 3.7  & 4    \\ \hline
                                                                         & \cellcolor[HTML]{FFFFFF}                        &                        & 41.5 & 30.6 & 10.9 \\ \cline{4-6}
                                                                         & \multirow{-2}{*}{\cellcolor[HTML]{FFFFFF}Train} & \multirow{-2}{*}{2841} & 1    & 8.2  & 7.8  \\ \cline{2-6}
                                                                         & \cellcolor[HTML]{FFFFFF}                        &                        & 41.7 & 33.3 & 11.7 \\ \cline{4-6}
\multirow{-4}{*}{\begin{tabular}[c]{@{}c@{}}Twitter \\ API\end{tabular}} & \multirow{-2}{*}{\cellcolor[HTML]{FFFFFF}Test}  & \multirow{-2}{*}{713}  & 3.3  & 6.7  & 3.3  \\ \hline
\end{tabular}
}
\caption{Total number of tweets and percentage of sarcastic labels in each dataset for subtask B.}
\label{tab:TaskB-Datasets}
\end{table}

\subsection{Augmented Datasets}
In addition to the original and external datasets, we created more datasets with more number of instances using the following methods:

\begin{enumerate}
  \item \textbf{Augmenting an original dataset with external datasets:}
    We added instances to an original dataset from the matching external datasets either to balance it or just to augment it, and we filtered out the NAN entries .
  \item \textbf{Augmenting a dataset using word embeddings:}
    For word embeddings we used Gensim library\footnote{\href{https://radimrehurek.com/gensim/}{Gensim Library}} together with GloVe word vectors\footnote{\href{https://github.com/RaRe-Technologies/gensim-data}{GloVe Word Vectors}} trained on two billion tweets with 100 dimension word vectors (glove-twitter-100). To create new instances in a dataset, we took a copy of one instance in the dataset and replaced up to four keywords in a tweet (or its rephrase) with one of the top three similar words according to the similarity between the word vectors, then we added the copied modified instance to the dataset and we repeated the process for other instances.
  \item \textbf{Augmenting a dataset by repeating instances:}
    Instances from a dataset are mostly repeated to balance the classes/labels in a dataset.
\end{enumerate}

The final datasets used for each subtask is explained in the dedicated section for the subtask.

\section{Methodology, Results and Analysis}

\subsection{Conventions} \label{Conventions}
In the following tables, if a table cell is highlighted with a light brown color, it means that the score is among the best results on the validation dataset in the corresponding section of the table; whereas the one with brown color is the highest score. Furthermore, if a cell is highlighted with a green color, it means that the score is our final submission score (released by the organizers) in the subtask on the test dataset; whereas the one with blue color is a score of a submitted model but not the final one (a team can have multiple submissions).

\subsection{Subtask A (English)}

\subsubsection{Datasets}
\begin{itemize}
  \item \textbf{Original}: The training split of the original dataset for English in Table \ref{tab:TaskA-Datasets} is splitted into training and validation datasets as shown in Table \ref{tab:TaskA-En-Original-Datasets}.
  \item \textbf{External}: As the measure for this task is F1-score for the sarcastic class, thus we created datasets which are biased towards the sarcastic class as shown in Table
\ref{tab:TaskA-En-External-Biased-Datasets}.
\end{itemize}

\begin{table}[h]
\centering
\resizebox{0.8\columnwidth}{!}{
\begin{tabular}{|c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{S\%}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{NS\%}} \\ \hline
Original Train &
  2080 &
  25 &
  75 \\ \hline
Original Val &
  1388 &
  25 &
  75 \\ \hline
\end{tabular}
}
\caption{Original datasets for subtask A (English) with the total number of tweets and the percentage of sarcastic (S\%) and non-sarcastic (NS\%) tweets.}
\label{tab:TaskA-En-Original-Datasets}
\end{table}

\begin{table}[h]
\resizebox{\columnwidth}{!}{
\begin{tabular}{|
>{\columncolor[HTML]{FFFFFF}}c |c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |c|c|}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Contributing\\ Datasets\end{tabular}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Additional Non\\ Sarcastic Tweets\\ from SemEval\\ 2018-Training\end{tabular}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{S\%}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{NS\%}} \\ \hline
B0 &  & 0    & 4578 & \cellcolor[HTML]{FFFFFF}66 & 34                         \\ \cline{1-1} \cline{3-6}
B1 &  & 145  & 4723 & 64                         & 36                         \\ \cline{1-1} \cline{3-6}
B2 &  & 290  & 4868 & \cellcolor[HTML]{FFFFFF}62 & \cellcolor[HTML]{FFFFFF}38 \\ \cline{1-1} \cline{3-6}
B3 &  & 435  & 5013 & \cellcolor[HTML]{FFFFFF}60 & \cellcolor[HTML]{FFFFFF}40 \\ \cline{1-1} \cline{3-6}
B4 &  & 580  & 5158 & \cellcolor[HTML]{FFFFFF}59 & \cellcolor[HTML]{FFFFFF}41 \\ \cline{1-1} \cline{3-6}
B5 &  & 725  & 5303 & \cellcolor[HTML]{FFFFFF}57 & \cellcolor[HTML]{FFFFFF}43 \\ \cline{1-1} \cline{3-6}
B6 &  & 870  & 5448 & \cellcolor[HTML]{FFFFFF}55 & \cellcolor[HTML]{FFFFFF}45 \\ \cline{1-1} \cline{3-6}
B7 &  & 1015 & 5593 & \cellcolor[HTML]{FFFFFF}54 & \cellcolor[HTML]{FFFFFF}46 \\ \cline{1-1} \cline{3-6}
B8 &  & 1160 & 5738 & \cellcolor[HTML]{FFFFFF}53 & \cellcolor[HTML]{FFFFFF}47 \\ \cline{1-1} \cline{3-6}
B9 &
  \multirow{-10}{*}{\begin{tabular}[c]{@{}c@{}}Original Train +\\ Twitter API Train\\ (only sarcastic) +\\ Twitter API Test \\ (only sarcastic) +\\ SemEval 2018 Train\\ (1911 sarcastic)\end{tabular}} &
  1305 &
  5883 &
  \cellcolor[HTML]{FFFFFF}51 &
  \cellcolor[HTML]{FFFFFF}49 \\ \hline
\end{tabular}
}
\caption{External datasets for subtask A (English).}
\label{tab:TaskA-En-External-Biased-Datasets}
\end{table}

\subsubsection{Approaches}
We primarily focused on the transformer based models. Since the task is a binary classification on tweets, the excellent choice to start with is BERTweet-base\footnote{\href{https://huggingface.co/vinai/bertweet-base}{HuggingFace Bertweet-Base}} and BERTweet-large\footnote{\href{https://huggingface.co/vinai/bertweet-large}{HuggingFace Bertweet-Large}} \cite{bertweet}, a pre-trained language model on 845M English Tweets. Likewise, we tried the ELECTRA\footnote{\href{https://huggingface.co/google/electra-large-discriminator}{HuggingFace Electra Large Discriminator}} \cite{clark2020electra} replaced token detection model (a pre-training task in which the model learns to distinguish real input tokens). In ELECTRA model, some tokens in the input are replaced with sample tokens instead of masking the tokens as in BERT. Moreover, we used a hierarchical network by passing the input tokens to the BERT model, then each token embedding is passed to a Bi-LSTM layer either with or without attention. The architecture of the BERT model, ELECTRA model, and hierarchical network is shown in appendix \ref{BERT-arch}, \ref{ELECTRA-arch}, and \ref{Hier_Network} respectively. The final layer of each model was a linear layer with softmax activation function and we used the cross entropy loss function.

\textbf{Note:} We ran several experiments on all the approaches of this subtask with different datasets and preprocessing types. We also experimented with different learning rates, epochs, and loss functions to verify which one is performing best.

\subsubsection{Results and Analysis}
Metric: The main metric is F1-score for the sarcastic class.

\textbf{BERT:}
We used BERTweet-large, as it gave better performance than BERTweet-base, on the original training dataset shown in Table \ref{tab:TaskA-En-Original-Datasets}. We experimented with different learning rates and preprocessing types, and ran for 5 epochs. The results are shown in Table \ref{tab:TaskA-En-Original-Results}.

\begin{table}[h]
\centering

\begin{tabular}{|
>{\columncolor[HTML]{FFFFFF}}c |c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Learning\\ Rate\end{tabular}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Type}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Val}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Test}} \\ \hline
\cellcolor[HTML]{FFFFFF}                           & I                          & 0.0057                         & 0.0293 \\ \cline{2-4}
\cellcolor[HTML]{FFFFFF}                           & \cellcolor[HTML]{FFFFFF}II & \cellcolor[HTML]{EADDCA}0.5017 & 0.457  \\ \cline{2-4}
\cellcolor[HTML]{FFFFFF}                           & III                        & 0                              & 0      \\ \cline{2-4}
\multirow{-4}{*}{\cellcolor[HTML]{FFFFFF}2 e - 6} & IV                         & 0                              & 0      \\ \hline
\cellcolor[HTML]{FFFFFF}                           & I                          & 0.3786                         & 0.5068 \\ \cline{2-4}
\cellcolor[HTML]{FFFFFF}                           & \cellcolor[HTML]{FFFFFF}II & \cellcolor[HTML]{EADDCA}0.5552 & 0.4874 \\ \cline{2-4}
\cellcolor[HTML]{FFFFFF}                           & III                        & \cellcolor[HTML]{EADDCA}0.5405 & 0.4972 \\ \cline{2-4}
\multirow{-4}{*}{\cellcolor[HTML]{FFFFFF}3 e - 6} & IV                         & 0                              & 0      \\ \hline
\cellcolor[HTML]{FFFFFF}                           & I                          & \cellcolor[HTML]{EADDCA}0.5585 & 0.4981 \\ \cline{2-4}
\cellcolor[HTML]{FFFFFF}                           & \cellcolor[HTML]{FFFFFF}II & 0.4926                         & 0.4717 \\ \cline{2-4}
\cellcolor[HTML]{FFFFFF}                           & III                        & \cellcolor[HTML]{EADDCA}0.5407 & 0.4772 \\ \cline{2-4}
\multirow{-4}{*}{\cellcolor[HTML]{FFFFFF}4 e - 6} & IV                         & 0                              & 0      \\ \hline
\cellcolor[HTML]{FFFFFF}                           & I                          & \cellcolor[HTML]{EADDCA}0.5275 & 0.4724 \\ \cline{2-4}
\cellcolor[HTML]{FFFFFF}                           & \cellcolor[HTML]{FFFFFF}II & \cellcolor[HTML]{EADDCA}0.5655 & 0.4841 \\ \cline{2-4}
\cellcolor[HTML]{FFFFFF}                           & III                        & 0                              & 0      \\ \cline{2-4}
\multirow{-4}{*}{\cellcolor[HTML]{FFFFFF}5 e - 6} & IV                         & 0                              & 0      \\ \hline
\end{tabular}

\caption{Results of the BERT model for subtask A (English) on original datasets}
\label{tab:TaskA-En-Original-Results}
\end{table}

From these experiments we found that Type-II preprocessing is performing better than other types and same applies for the learning rate 4e-6. We conducted similar experiments on the external datasets in Table \ref{tab:TaskA-En-External-Biased-Datasets} and we found similar results. We tried using the cross entropy loss function with and without weights on the external datasets using the same learning rate, preprocessing type, and number of epochs. We got our best result on the B4 dataset with weighted loss function which was our official submission score for this subtask. The results are shown in Table \ref{tab:TaskA-En-Biased-Results}.

\begin{table}[h]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c|
>{\columncolor[HTML]{FFFFFF}}c
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \multicolumn{2}{c|}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Loss1: Without\\ Weights\end{tabular}}}} &
  \multicolumn{2}{c|}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Loss2: W1=1/\#NS,\\W2=1/\#S\end{tabular}}}} \\ \cline{2-5}
\multirow{-3}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Biased}}} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Val}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Test}} &
  \multicolumn{1}{c|}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Val}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Test}} \\ \hline
B0 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5784} &
  0.4487 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5944} &
  0.4519 \\ \hline
B1 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5714} &
  0.4548 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5738} &
  0.479 \\ \hline
B2 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5767} &
  0.465 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5951} &
  0.4917 \\ \hline
B3 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{D5AB6D}0.601} &
  \cellcolor[HTML]{6D9EEB}0.4626 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5931} &
  0.4817 \\ \hline
B4 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5954} &
  0.4727 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{D5AB6D}0.6025} &
  \cellcolor[HTML]{6AA84F}0.4769 \\ \hline
B5 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5874} &
  0.5142 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5803} &
  0.5008 \\ \hline
B6 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5858} &
  0.4791 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5624} &
  0.4884 \\ \hline
B7 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5957} &
  0.5016 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5637} &
  0.5034 \\ \hline
B8 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.584} &
  0.492 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5814} &
  0.5 \\ \hline
B9 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5723} &
  0.487 &
  \multicolumn{1}{c|}{\cellcolor[HTML]{FFFFFF}0.5554} &
  0.49  \\ \hline
\end{tabular}
}
\caption{Results of the BERT model for subtask A (English) on external datasets.}
\label{tab:TaskA-En-Biased-Results}
\end{table}

We used 5 epochs and 4e-6 learning rate because they gave the best results as shown in appendix \ref{Learning_Rates_Epochs}.

The official scores and leader-board ranks of the teams for subtask A (English) are shown in Table \ref{tab:leader-board-subtask-A-English}.
\begin{table}[h]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c|l|c|}
\hline
\rowcolor[HTML]{739CB9}
{\color[HTML]{FFFFFF} \textbf{Rank}} & {\color[HTML]{FFFFFF} \textbf{User}}                                  & \multicolumn{1}{l|}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{F-1 sarcastic}}} \\ \hline
\rowcolor[HTML]{FFFFFF}
1                                    & stce                                                                  & 0.6052                                                                                \\ \hline
\rowcolor[HTML]{FFFFFF}
2                                    & emma                                                                  & 0.5691                                                                                \\ \hline
\rowcolor[HTML]{FFFFFF}
3                                    & saroyehun                                                             & 0.5295                                                                                \\ \hline
\rowcolor[HTML]{FFFFFF}
\textbf{4}                           & \textbf{\begin{tabular}[c]{@{}l@{}}ShubhamKumarNigam\end{tabular}} & \textbf{0.4769}                                                                            \\ \hline
\end{tabular}
}
\caption{Scores and leader-board ranks for subtask A (English)}
\label{tab:leader-board-subtask-A-English}
\end{table}

\textbf{ELECTRA:}
We used the ELECTRA model on the external datasets with Type-II preprocessing, 6e-6 learning rate, and 5 epochs as they were performing the best as shown in Table \ref{tab:TaskA-En-ELECTRA}.

\begin{table}[h]
\centering
\begin{tabular}{|c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Biased}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Val}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Test}} \\ \hline
B0                                                           & 0.5525                         & 0.4684                         \\ \hline
B1                                                           & 0.4002                         & 0.25                           \\ \hline
B2                                                           & 0.5738                         & 0.4762                         \\ \hline
B3                                                           & 0.4002                         & 0.25                           \\ \hline
B4                                                           & \cellcolor[HTML]{D5AB6D}0.5756 & \cellcolor[HTML]{6D9EEB}0.4879 \\ \hline
B5                                                           & 0.4002                         & 0.25                           \\ \hline
B6                                                           & 0.5468                         & 0.4642                         \\ \hline
B7                                                           & 0.5702                         & 0.4789                         \\ \hline
B8                                                           & 0.479                          & 0.5073                         \\ \hline
B9                                                           & 0.4002                         & 0.25                           \\ \hline
\end{tabular}
\caption{Results of the ELECTRA model for subtask A (English) on external datasets.}
\label{tab:TaskA-En-ELECTRA}
\end{table}

\textbf{BERT+BiLSTM with and without attention:}
The results we got using this architecture was not much stable (i.e. they may change when re-running the experiment) and thus we did not use this model for the official submission. More details about the results of the model can be found in the appendix \ref{BERT-BiLSTM-appendix}.

\subsection{Subtask A (Arabic)}

\subsubsection{Datasets}
\textbf{Original:}
The training split of the original dataset for Arabic in Table \ref{tab:TaskA-Datasets} is splitted into training and validation datasets as shown in Table \ref{tab:TaskA-Ar-Original-Datasets}.

\begin{table}[h]
\centering
\resizebox{0.8\columnwidth}{!}{
\begin{tabular}{|c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{S\%}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{NS\%}} \\ \hline
Original Train &
  1861 &
  24 &
  76 \\ \hline
Original Val &
  1241 &
  24 &
  76 \\ \hline
\end{tabular}
}
\caption{Original datasets for subtask A (Arabic).}
\label{tab:TaskA-Ar-Original-Datasets}
\end{table}

\textbf{External:}
Same as in subtask A (English), we created datasets which are biased towards the sarcastic class as shown in Table
\ref{tab:TaskA-Ar-External-Biased-Datasets}.

\begin{table}[h]
\resizebox{\columnwidth}{!}{
\begin{tabular}{|
>{\columncolor[HTML]{FFFFFF}}c |c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Contributing\\ Datasets\end{tabular}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Additional Non\\ Sarcastic Tweets\\ from SemEval\\ 2018-Training\end{tabular}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{S\%}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{NS\%}} \\ \hline
B0 &  & 0    & 4850 & 71 & 29 \\ \cline{1-1} \cline{3-6}
B1 &  & 202  & 5052 & 68 & 32 \\ \cline{1-1} \cline{3-6}
B2 &  & 404  & 5254 & 65 & 35 \\ \cline{1-1} \cline{3-6}
B3 &  & 606  & 5456 & 63 & 37 \\ \cline{1-1} \cline{3-6}
B4 &  & 808  & 5658 & 61 & 39 \\ \cline{1-1} \cline{3-6}
B5 &  & 1010 & 5860 & 59 & 41 \\ \cline{1-1} \cline{3-6}
B6 &  & 1212 & 6062 & 57 & 43 \\ \cline{1-1} \cline{3-6}
B7 &  & 1414 & 6264 & 55 & 45 \\ \cline{1-1} \cline{3-6}
B8 &  & 1616 & 6466 & 53 & 47 \\ \cline{1-1} \cline{3-6}
B9 &
  \multirow{-10}{*}{\begin{tabular}[c]{@{}c@{}}Original Train+\\ ArSarcasm-v2 \\ Train\\ (only sarcastic)+\\ ArSarcasm-v2 \\ Test\\ (821 sarcastic)\end{tabular}} &
  1818 &
  6668 &
  52 &
  48 \\ \hline
\end{tabular}
}
\caption{External datasets for subtask A (Arabic)}
\label{tab:TaskA-Ar-External-Biased-Datasets}
\end{table}

\subsubsection{Approaches}
The approached used here are similar to subtask A (English) except for the used transformers. Since the data is in the Arabic language, we tried some models from The \textbf{C}omputational \textbf{A}pproaches to \textbf{M}od\textbf{e}ling \textbf{L}anguage (CAMeL) research lab \footnote{\href{https://huggingface.co/CAMeL-Lab}{HuggingFace CAMeL-Lab}}. They majorly focused on Arabic and Arabic dialect processing, machine translation, text analysis, and dialogue systems.

The models are available on the Hugging Face library. CAMeLBERT is a collection of BERT models pre-trained on Arabic texts with different sizes and variants \cite{inoue-etal-2021-interplay}. They released pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA). We tried CAMeLBERT-DA and CAMeLBERT-Mix for sarcasm detection. Likewise, we tried the AraBERT v2 which is a pre-trained BERT based on Google's BERT architecture for Arabic Language Understanding\footnote{\href{https://huggingface.co/aubmindlab/bert-base-arabertv02}{HuggingFace Bert-Base-Arabert-v02}} \cite{antoun2020arabert}.

\subsubsection{Results and Analysis}
Metric: The main metric is F1-score for the sarcastic class.

\textbf{BERT:}
We used CAMeLBERT-Mix as it performed the best among other BERT models. We applied it on the external datasets with non-weighted cross entropy loss function, 5 epochs, and 2e-5 learning rate because they gave the best results which are shown in Table \ref{tab:TaskA-Ar-Biased-Results}.

\begin{table}[h]
\centering
\begin{tabular}{|c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Biased}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Val}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Test}} \\ \hline
B0 & 0.7168 & 0.3438 \\ \hline
B1 & 0.7025 & 0.4163 \\ \hline
B2 &
  \cellcolor[HTML]{D5AB6D}0.7131 &
  \cellcolor[HTML]{6AA84F}0.4071 \\ \hline
B3 & 0.6804 & 0.4335 \\ \hline
B4 & 0.7015 & 0.4186 \\ \hline
B5 & 0.6927 & 0.4332 \\ \hline
B6 & 0.7012 & 0.4048 \\ \hline
B7 & 0.7124 & 0.4365 \\ \hline
B8 & 0.6731 & 0.4589 \\ \hline
B9 & 0.7094 & 0.4589 \\ \hline
\end{tabular}
\caption{Results of the BERT model for subtask A
(Arabic) on external datasets.}
\label{tab:TaskA-Ar-Biased-Results}
\end{table}

\textbf{BERT+BiLSTM+Attention:}
Same in subtask A (English), We used attention with BiLSTM on top of BERT model. The results also were not much stable. However, the best results for this architecture occurred when using 5 epochs and 9e-6 learning rate on B3 and B9 datasets as shown in Table \ref{tab:TaskA-Ar-BERT-RNN-ATT}.

\begin{table}[h]
\centering
\begin{tabular}{|c|c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{6D9EEB}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Biased}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Hidden\\ State Size\end{tabular}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Val}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Test}} \\ \hline
B3 &
  50 &
  0.6849 &
  0.4234 \\ \hline
B9 &
  1000 &
  0.7123 &
  0.4693 \\ \hline
\end{tabular}
\caption{Results of the BERT+BiLSTM+Attention model for subtask A (Arabic)}
\label{tab:TaskA-Ar-BERT-RNN-ATT}
\end{table}

The official scores and leader-board ranks of the teams for subtask A (Arabic) are shown in Table \ref{tab:leader-board-subtask-A-Arabic}.

\begin{table}[h]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c|l|c|}
\hline
\rowcolor[HTML]{648CA8}
{\color[HTML]{FFFFFF} \textbf{Rank}} & {\color[HTML]{FFFFFF} \textbf{User}}                                  & \multicolumn{1}{l|}{\cellcolor[HTML]{648CA8}{\color[HTML]{FFFFFF} \textbf{F-1 sarcastic}}} \\ \hline
\rowcolor[HTML]{FFFFFF}
1                                    & Abdelkader                                                            & 0.5632                                                                                \\ \hline
\rowcolor[HTML]{FFFFFF}
2                                    & Aya                                                                   & 0.5076                                                                                \\ \hline
\rowcolor[HTML]{FFFFFF}
3                                    & rematchka                                                             & 0.4767                                                                                \\ \hline
\rowcolor[HTML]{FFFFFF}
\textbf{10}                          & \textbf{\begin{tabular}[c]{@{}l@{}}ShubhamKumarNigam\end{tabular}} & \textbf{0.4072}                                                                            \\ \hline
\end{tabular}
}
\caption{Scores and leader-board ranks for subtask A
(Arabic)}
\label{tab:leader-board-subtask-A-Arabic}
\end{table}

\subsection{Subtask B}

\subsubsection{Datasets}
\textbf{Original:}
The training split of the original dataset for English in Table \ref{tab:TaskB-Datasets} is splitted into training and validation datasets as shown in Table \ref{tab:TaskB-En-Original-Datasets}.

\begin{table}[h]
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Sarcasm}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Irony}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Satire}} \\ \cline{3-5}
\multirow{-2}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}}} &
  \multirow{-2}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Under-\\ statement\end{tabular}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Over-\\ statement\end{tabular}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Rhetorical\\ question\end{tabular}}} \\ \hline
                                                                           & \cellcolor[HTML]{FFFFFF}                      & 67.60\% & 15.10\% & 2.60\%  \\ \cline{3-5}
\multirow{-2}{*}{\begin{tabular}[c]{@{}c@{}}Original\\ Train\end{tabular}} & \multirow{-2}{*}{\cellcolor[HTML]{FFFFFF}606} & 1\%     & 3.50\%  & 10.20\% \\ \hline
                                                                           & \cellcolor[HTML]{FFFFFF}                      & 70\%    & 14.20\% & 1.90\%  \\ \cline{3-5}
\multirow{-2}{*}{\begin{tabular}[c]{@{}c@{}}Original\\ Val\end{tabular}}   & \multirow{-2}{*}{\cellcolor[HTML]{FFFFFF}261} & 1\%     & 4.50\%  & 8.40\%  \\ \hline
\end{tabular}
}
\caption{Original datasets for subtask B (English)}
\label{tab:TaskB-En-Original-Datasets}
\end{table}

\textbf{External:}
The original and external datasets presented in Table \ref{tab:TaskB-Datasets} (without the validation dataset) were added together to form a new dataset (Ext-NB). Then the resulting dataset was balanced either by using word embeddings (Ext-UW) or by repeating instances (Ext-UR). We created a dataset (Ext-EB) to give more importance to the labels of low number of instances by repeating the instances of these labels up to the limits specified by these heuristic formulas:\\
{\small \#irony=\#sarcasm*(1+1/sqrt(\#irony)) \hfill (1)}
{\small \#satire=\#sarcasm*(1+2/sqrt(\#satire))\hfill (2)}\\
{\small \#understatement=\#sarcasm*(1+3/sqrt(understatement)) \hfill (3)}\\
{\small \#overstatement=\#sarcasm*(1+1.5/sqrt(overstatement)) \hfill (4)}\\
{\small \#rhetorical=\#sarcasm*(1+1.2/sqrt(\#rhetorical)) \hfill (5)}\\
The datasets are shown in Table \ref{tab:TaskB-En-External-Datasets}.

\begin{table}[h]
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c|c|c|
>{\columncolor[HTML]{FFFFFF}}c |c|
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Sarcasm}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Under-\\ statement\end{tabular}}} \\ \cline{5-6}
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} } &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Irony}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Over-\\ statement\end{tabular}}} \\ \cline{5-6}
\multirow{-5}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}}} &
  \multirow{-5}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Balanced}}} &
  \multirow{-5}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Contributing\\ Datasets\end{tabular}}}} &
  \multirow{-5}{*}{\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Satire}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{\begin{tabular}[c]{@{}c@{}}Rhetorical\\ question\end{tabular}}} \\ \hline
\cellcolor[HTML]{FFFFFF} &
  \cellcolor[HTML]{FFFFFF} &
   &
  \cellcolor[HTML]{FFFFFF} &
  \cellcolor[HTML]{FFFFFF}55.90\% &
  1.20\% \\ \cline{5-6}
\cellcolor[HTML]{FFFFFF} &
  \cellcolor[HTML]{FFFFFF} &
   &
  \cellcolor[HTML]{FFFFFF} &
  \cellcolor[HTML]{FFFFFF}22.30\% &
  5.50\% \\ \cline{5-6}
\multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}Ext-NB} &
  \multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}Not Balanced} &
   &
  \multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}1203} &
  \cellcolor[HTML]{FFFFFF}6.40\% &
  8.70\% \\ \cline{1-2} \cline{4-6}
 &
   &
   &
  \cellcolor[HTML]{FFFFFF} &
  16.50\% &
  16.60\% \\ \cline{5-6}
 &
   &
   &
  \cellcolor[HTML]{FFFFFF} &
  \cellcolor[HTML]{FFFFFF}16.50\% &
  16.80\% \\ \cline{5-6}
\multirow{-3}{*}{Ext-UW} &
  \multirow{-3}{*}{\begin{tabular}[c]{@{}c@{}}Using Word\\ Embedding\end{tabular}} &
   &
  \multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}4336} &
  \cellcolor[HTML]{FFFFFF}16.60\% &
  17\% \\ \cline{1-2} \cline{4-6}
 &
   &
   &
  \cellcolor[HTML]{FFFFFF} &
  16.50\% &
  16.60\% \\ \cline{5-6}
 &
   &
   &
  \cellcolor[HTML]{FFFFFF} &
  \cellcolor[HTML]{FFFFFF}16.50\% &
  16.80\% \\ \cline{5-6}
\multirow{-3}{*}{Ext-UR} &
  \multirow{-3}{*}{\begin{tabular}[c]{@{}c@{}}Using\\ Repetition\end{tabular}} &
   &
  \multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}4336} &
  \cellcolor[HTML]{FFFFFF}16.60\% &
  16.90\% \\ \cline{1-2} \cline{4-6}
 &
  \cellcolor[HTML]{FFFFFF} &
   &
  \cellcolor[HTML]{FFFFFF} &
  15\% &
  18.80\% \\ \cline{5-6}
 &
  \cellcolor[HTML]{FFFFFF} &
   &
  \cellcolor[HTML]{FFFFFF} &
  \cellcolor[HTML]{FFFFFF}15.80\% &
  17\% \\ \cline{5-6}
\multirow{-3}{*}{Ext-EB} &
  \multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}Not Balanced} &
  \multirow{-12}{*}{\begin{tabular}[c]{@{}c@{}}Original\\ Train +\\ Twitter API\\ Train +\\ Twitter API\\ Test\end{tabular}} &
  \multirow{-3}{*}{\cellcolor[HTML]{FFFFFF}5314} &
  \cellcolor[HTML]{FFFFFF}16.70\% &
  16.80\% \\ \hline
\end{tabular}
}
\caption{External datasets for subtask B (English).}
\label{tab:TaskB-En-External-Datasets}
\end{table}

\subsubsection{Approaches}
This subtask primarily focused on BERTweet-large. As it is a multi labeling subtask, we used sigmoid as the activation function in the last layer and binary cross entropy as the loss function.

\subsubsection{Results and Analysis}
Metric: The main metric is Macro-F1 score.

\textbf{BERT:}
We used BERTweet-large on the external datasets using 5 epochs, 6e-6 learning rate, and Type-II preprocessing. The results are shown in Table \ref{tab:TaskB-En-External-Results}.

\begin{table}[h]
\centering
\begin{tabular}{|c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Val}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Test}} \\ \hline
\cellcolor[HTML]{FFFFFF}Ext-NB & 0.1513                         & 0.038                          \\ \hline
Ext-UW                         & 0.318                          & 0.0716                         \\ \hline
Ext-UR                         & 0.3412                         & 0.076                          \\ \hline
Ext-EB                         & \cellcolor[HTML]{D5AB6D}0.4152 & \cellcolor[HTML]{6AA84F}0.0778 \\ \hline
\end{tabular}
\caption{Results of the BERT model for subtask B
(English) on external datasets.}
\label{tab:TaskB-En-External-Results}
\end{table}

The official scores and leader-board ranks of the teams for subtask B (English) are shown in Table \ref{tab:leader-board-subtask-B-English}.

\begin{table}[h]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c|l|r|}
\hline
\rowcolor[HTML]{648CA8}
{\color[HTML]{FFFFFF} \textbf{Rank}} & {\color[HTML]{FFFFFF} \textbf{User}}                                  & \multicolumn{1}{l|}{\cellcolor[HTML]{648CA8}{\color[HTML]{FFFFFF} \textbf{macro F-score}}} \\ \hline
\rowcolor[HTML]{FFFFFF}
1                                    & Duxy                                                                  & 0.163                                                                                \\ \hline
\rowcolor[HTML]{FFFFFF}
2                                    & Abdelkader                                                            & 0.0875                                                                                \\ \hline
\rowcolor[HTML]{FFFFFF}
3                                    & robvanderg                                                            & 0.0851                                                                                \\ \hline
\rowcolor[HTML]{FFFFFF}
\textbf{6}                           & \textbf{\begin{tabular}[c]{@{}l@{}}ShubhamKumarNigam\end{tabular}} & \textbf{0.0778}                                                                            \\ \hline
\end{tabular}
}
\caption{Scores and leader-board ranks for subtask B (English)}
\label{tab:leader-board-subtask-B-English}
\end{table}
\subsection{Subtask C (English)}

\subsubsection{Datasets}
\textbf{Original:}
The training split of the original dataset for English in Table \ref{tab:TaskA-Datasets} is splitted into training and validation datasets. As we do not have external datasets here, so we augmented the training split once with word embeddings (Original-Embedding) and once with repetition (Original-Repetition). We swapped between the tweet and its rephrase for half of the instances together with flipping the value of the sarcastic field, so that the model will be able to learn. Otherwise, it may always predict the first text as the tweet and the second one as the rephrase. The datasets are shown in Table \ref{tab:TaskC-En-Original-Datasets}.

\begin{table}[h]
\centering
\begin{tabular}{|c|
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}} & \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}} \\ \hline
Original-Train   & 606  \\ \hline
Original-Validation & 261  \\ \hline
Original-Embedding  & 1606 \\ \hline
Original-Repetition & 1606 \\ \hline
\end{tabular}
\caption{Original datasets for subtask C (English)}
\label{tab:TaskC-En-Original-Datasets}
\end{table}

\subsubsection{Approaches}
Organizers provide a sarcastic text, and its non-sarcastic rephrase, i.e., two texts convey the same meaning. Since the input format changed in this subtask, we input both texts together to the BERT model as one text separating them by the separating token. We focused on transformers which are trained on question-answering tasks. We tried BERT models trained on the \textbf{S}tanford \textbf{Qu}estion \textbf{A}nswering \textbf{D}ataset (SQuAD) dataset \cite{rajpurkar-etal-2018-know}. SQuAD is a reading comprehension dataset consisting of questions posed by crowdworkers on a set of Wikipedia articles.

We took models from the Hugging Face library; one is BERT large model (cased)\footnote{\href{https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad}{HuggingFace Bert-Large-Cased-Whole-Word-Masking-Finetuned-Squad}}, trained on whole word masking, and fine-tuned on the SQuAD dataset. Another is BERT base model (uncased)\footnote{\href{https://huggingface.co/twmkn9/bert-base-uncased-squad2}{HuggingFace Bert-Base-Uncased-Squad2}}, trained on Masked language modeling (MLM), and fine-tuned on the SQuAD dataset.

\subsubsection{Results and Analysis}
Metric: The main metric is the accuracy.

\textbf{BERT:}
We used BERT large model (cased) on the original datasets, because it gave better results among the other models, using 15 epochs, 8e-6 learning rate, and the cross entropy as the loss function. The results are shown in Table \ref{tab:TaskC-En-Original-Results}. We did our submission using Type-I preprocessing even though Type-II was performing better on the validation dataset.

\begin{table}[h]
\centering
\begin{tabular}{|c|c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Type}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Val}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Test}} \\ \hline
 &
  \cellcolor[HTML]{FFFFFF}I &
  \cellcolor[HTML]{6D9EEB}0.951 &
  \cellcolor[HTML]{6AA84F}0.79 \\ \cline{2-4}
 &
  \cellcolor[HTML]{FFFFFF}II &
  0.9395 &
  0.8 \\ \cline{2-4}
 &
  \cellcolor[HTML]{FFFFFF}III &
  0.9193 &
  0.83 \\ \cline{2-4}
\multirow{-4}{*}{\begin{tabular}[c]{@{}c@{}}Original-\\ Train\end{tabular}} &
  \cellcolor[HTML]{FFFFFF}IV &
  0.9135 &
  0.79 \\ \hline
 &
  I &
  0.9654 &
  0.83 \\ \cline{2-4}
 &
  \cellcolor[HTML]{FFFFFF}II &
  0.9654 &
  0.8 \\ \cline{2-4}
 &
  III &
  0.9366 &
  0.82 \\ \cline{2-4}
\multirow{-4}{*}{\begin{tabular}[c]{@{}c@{}}Original-\\ Embedding\end{tabular}} &
  IV &
  0.8588 &
  0.72 \\ \hline
 &
  I &
  0.9395 &
  0.8 \\ \cline{2-4}
 &
  \cellcolor[HTML]{FFFFFF}II &
  0.9222 &
  0.815 \\ \cline{2-4}
 &
  III &
  0.9078 &
  0.8 \\ \cline{2-4}
\multirow{-4}{*}{\begin{tabular}[c]{@{}c@{}}Original-\\ Repetition\end{tabular}} &
  IV &
  0.9078 &
  0.68 \\ \hline
\end{tabular}
\caption{Results of the BERT model for subtask C (English) on original datasets.}
\label{tab:TaskC-En-Original-Results}
\end{table}

The official scores and leader-board ranks of the teams for subtask C (English) are shown in Table \ref{tab:leader-board-subtask-C-English}.

\begin{table}[h]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c|l|r|}
\hline
\rowcolor[HTML]{648CA8}
{\color[HTML]{FFFFFF} \textbf{Rank}} &
  {\color[HTML]{FFFFFF} \textbf{User}} &
  \multicolumn{1}{l|}{\cellcolor[HTML]{648CA8}{\color[HTML]{FFFFFF} \textbf{Accuracy}}} \\ \hline
\rowcolor[HTML]{FFFFFF}
1 & emma      & 0.87  \\ \hline
\rowcolor[HTML]{FFFFFF}
2 & lizefeng  & 0.855 \\ \hline
\rowcolor[HTML]{FFFFFF}
3 & leon14138 & 0.805 \\ \hline
\rowcolor[HTML]{FFFFFF}
\textbf{4} &
  \textbf{\begin{tabular}[c]{@{}l@{}}ShubhamKumarNigam\end{tabular}} &
  \textbf{0.79} \\ \hline
\end{tabular}
}
\caption{Scores and leader-board ranks for subtask C
(English)}
\label{tab:leader-board-subtask-C-English}
\end{table}

\subsection{Subtask C (Arabic)}

\subsubsection{Datasets}
\textbf{Original:}
The datasets were generated in the same way as in subtask C (Engligh) and are shown in Table \ref{tab:TaskC-Ar-Original-Datasets}

\begin{table}[h]
\centering
\begin{tabular}{|c|
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}} & \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Total}} \\ \hline
Original-Train   & 521  \\ \hline
Original-Validation & 224  \\ \hline
Original-Embedding  & 1521 \\ \hline
Original-Repetition & 1521 \\ \hline
\end{tabular}
\caption{Original datasets for subtask C (Arabic)}
\label{tab:TaskC-Ar-Original-Datasets}
\end{table}

\subsubsection{Approaches}
The approached used here are similar to subtask C (English) except for the used transformers. Since the data is in the Arabic language, we took a couple of models from the Hugging Face library like the multilingual model mBERT base (cased), trained on the QA dataset in seven languages and fine-tuned on the combination of XQuAD \cite{artetxe-etal-2020-cross} and MLQA \cite{lewis-etal-2020-mlqa} datasets. We compared their performance to the CAMeLBERT-Mix model.

\subsubsection{Results and Analysis}
Metric: The main metric is the accuracy.

\textbf{BERT:}
We used CAMeLBERT-Mix model on the original datasets using 5 epochs, 3e-5 learning rate, and the cross entropy as the loss function. The results are shown in Table \ref{tab:TaskC-Ar-Original-Results}. We did our submission using Type-II preprocessing as it is the best performing.

\begin{table}[h]
\centering
\begin{tabular}{|c|c|
>{\columncolor[HTML]{FFFFFF}}c |
>{\columncolor[HTML]{FFFFFF}}c |}
\hline
\cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Dataset}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Type}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Val}} &
  \cellcolor[HTML]{739CB9}{\color[HTML]{FFFFFF} \textbf{Test}} \\ \hline
 &
  I &
  0.6242 &
  0.5 \\ \cline{2-4}
 &
  \cellcolor[HTML]{FFFFFF}II &
  0.6242 &
  0.5 \\ \cline{2-4}
 &
  III &
  0.6711 &
  0.72 \\ \cline{2-4}
\multirow{-4}{*}{\begin{tabular}[c]{@{}c@{}}Original-\\ Train\end{tabular}} &
  IV &
  0.3758 &
  0.5 \\ \hline
 &
  I &
  0.8792 &
  0.825 \\ \cline{2-4}
 &
  \cellcolor[HTML]{FFFFFF}II &
  0.8792 &
  0.845 \\ \cline{2-4}
 &
  III &
  0.8691 &
  0.845 \\ \cline{2-4}
\multirow{-4}{*}{\begin{tabular}[c]{@{}c@{}}Original-\\ Embedding\end{tabular}} &
  IV &
  0.7517 &
  0.71 \\ \hline
 &
  I &
  0.8993 &
  0.855 \\ \cline{2-4}
 &
  \cellcolor[HTML]{FFFFFF}II &
  \cellcolor[HTML]{6D9EEB}0.9262 &
  \cellcolor[HTML]{6AA84F}0.87 \\ \cline{2-4}
 &
  III &
  0.8658 &
  0.86 \\ \cline{2-4}
\multirow{-4}{*}{\begin{tabular}[c]{@{}c@{}}Original-\\ Repetition\end{tabular}} &
  IV &
  0.6409 &
  0.705 \\ \hline
\end{tabular}
\caption{Results of BERT model for subtask C (Arabic) on original datasets}
\label{tab:TaskC-Ar-Original-Results}
\end{table}

The official scores and leader-board ranks of the teams for subtask C (Arabic) is shown in Table \ref{tab:leader-board-subtask-C-Arabic}.

\begin{table}[h]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{|c|l|r|}
\hline
\rowcolor[HTML]{648CA8}
{\color[HTML]{FFFFFF} \textbf{Rank}} &
  {\color[HTML]{FFFFFF} \textbf{User}} &
  \multicolumn{1}{l|}{\cellcolor[HTML]{648CA8}{\color[HTML]{FFFFFF} \textbf{Accuracy}}} \\ \hline
\rowcolor[HTML]{FFFFFF}
1 & lizefeng      & 0.93  \\ \hline
\rowcolor[HTML]{FFFFFF}
2 & AlamiHamza    & 0.885 \\ \hline
\rowcolor[HTML]{FFFFFF}
3 & maryam.najafi & 0.875 \\ \hline
\rowcolor[HTML]{FFFFFF}
\textbf{4} &
  \textbf{\begin{tabular}[c]{@{}l@{}}ShubhamKumarNigam\end{tabular}} &
  \textbf{0.87} \\ \hline
\end{tabular}
}
\caption{Scores and leader-board ranks for subtask C
(Arabic)}
\label{tab:leader-board-subtask-C-Arabic}
\end{table}

\section{Introduction}
Sarcasm is a figurative language where speakers or writers usually mean the contrary of what they say. Recognizing whether a speaker or writer is sarcastic is essential to downstream applications to understand the sentiments, opinions, and beliefs correctly \cite{ghosh-etal-2020-report}. Sarcasm is ubiquitous on the social media text and, due to its nature, can be highly divisive of computational systems that perform tasks on that kind of data such as sentiment analysis, opinion mining, and harassment detection \cite{van-hee-etal-2018-semeval, bing2012sentiment,rosenthal-etal-2014-semeval,maynard-greenwood-2014-cares}.

Our team Plumeria participated in SemEval 2022 task 6 \cite{abufarha-etal-2022-semeval} in all its subtasks on English and Arabic. Previous shared tasks on sarcasm detection \cite{VanHee2018, ghanem2019idat,ghosh-etal-2020-report,abu-farha-etal-2021-overview} have only two subtasks; one is sarcasm detection and another is predicting the type of sarcasm. However, in SemEval 2022 task 6 \cite{abufarha-etal-2022-semeval} organizers formulate three subtasks for both languages following the methods described in \cite{oprea-magdy-2020-isarcasm}.

Using the two datasets for English and Arabic, organizers formulate three subtasks as follows:
\begin{itemize}
    \item \textbf{Subtask A (English and Arabic):} It is a binary classification subtask where submitted systems have to predict whether a tweet is sarcastic or not.
    \item \textbf{Subtask B (for English only):} It is a multi-label classification subtask where submitted systems have to predict one or more labels out of six ironic-speech labels: sarcasm, irony, satire, understatement, overstatement, and rhetorical question.
    \item \textbf{Subtask C (English and Arabic):} It is a binary classification subtask. Given two texts, a sarcastic tweet and its non-sarcastic rephrase which conveys the same meaning, submitted systems have to predict which text is the sarcastic one.
\end{itemize}

    In this shared task, the systems we submitted for all three subtasks primarily focused on the transformer based approaches because of their success in the field of NLP. Moreover, we used a hierarchical network by stacking a BiLSTM layer on top of a transformer either with or without attention mechanism. However, the most significant constraint of a neural network is that they need lots of data for training to give satisfactory results. In addition, if the dataset is imbalanced with fewer instances of some class, this can result in poor results for detecting this class. That motivates us to create biased datasets, towards the concerned class/label, from existing datasets by increasing the number of instances of such a class/label. A detailed explanation of dataset creation is in section \ref{dataset}. We also illustrate the composition of created datasets in each subtask and the performance of the models on them.

    The rank of our team was consistent across most subtasks (fourth rank in three subtasks, sixth rank in one subtask, and tenth rank in one subtask); unlike many teams which scored high in one or two subtasks but scored considerably less in other subtasks. This shows that the robustness and the consistency of our methods. The bias in created datasets was crucial for subtask A and subtask B, while augmenting a dataset plays a key role in subtask C. We released the codes and datasets for all subtasks via GitHub\footnote{\url{https://github.com/ShubhamKumarNigam/iSarcasm-SemEval-2022-Task-6}}.

\section{Related Work}

\subsection{Previous Shared Tasks}

Previous shared tasks on sarcasm detection \cite{VanHee2018}; \cite{ghanem2019idat}; \cite{ghosh-etal-2020-report}; \cite{abu-farha-etal-2021-overview} have only two subtasks; one is sarcasm detection and another is to predict the type of sarcasm.

In shared task \cite{VanHee2018}, the systems submitted for both subtasks represent a variety of neural-network-based approaches (i.e., CNNs, RNNs, and (bi-)LSTMs) exploiting word and character embeddings and handcrafted features. Some participants also included other popular classification algorithms: Support Vector Machines, Maximum Entropy, Random Forest, and Naive Bayes.

In shared task \cite{ghanem2019idat}, the methods of participants submitted mostly similar to \cite{VanHee2018}, ranged from feature-based to neural networks using either classical machine learning techniques or ensemble methods. Some participants also used Transformer based models.

Arabic sarcasm detection

This shared task \cite{ghanem2019idat} for Arabic sarcasm detection, in that task, most of the teams used pre-trained language models such as AraBERT \cite{antoun-etal-2020-arabert}, and MARBERT \cite{abdul-mageed-etal-2021-arbert}. A few of the participants used deep learning and conventional machine learning approaches. Additionally, participants utilized a language model to augment the data with new sarcastic examples. The augmentation was done by replacing and adding new words. Some participants used BERT models in different ways. The majority of the participants used ensemble methods, combining BERT-based models with other models. While most participants used the same architecture for both tasks, some participants relied on multi-task learning to train the model on both tasks simultaneously

In shared task \cite{ghosh-etal-2020-report}, almost every submitted system have used transformer-based architectures, such as
BERT \cite{} and RoBERTa \cite{}, and other variants, emphasize the increasing popularity of using pre-trained language models for various classification tasks. However, the best systems have employed a clever mix of ensemble techniques and data augmentation setups.

redpajama_set_name: RedPajamaArXiv


text:
Plug and play: how wind projects progress under the turnkey model
1 November 2012 by Suhas Tendulkar
INDIA: India is now the third largest wind market by annual installations, adding 3GW in 2011 after China (17.6GW) and the US (6.8GW), according to the Global Wind Energy Council. It is also a unique market where developers and turbine manufacturers are often synonymous.
Suzlon delivers completed wind farms featuring its own turbines
Pune-headquartered Suzlon has been credited with being a catalyst in India's rapid rise in installations. Key to this was the firm's business model, which runs from component manufacture through to full development and operation of the project.
Even though Suzlon started as a core manufacturer of wind turbines, 15 years ago it saw the value in providing end-to-end support right from turbine purchase to commissioning, known as turnkey. This was particularly useful in the initial years of the wind industry in India, when there was little understanding of the complicated legal and administrative processes that come with a wind project, from land acquisition, wind resource monitoring and site development, federal and local clearances and grid integration.
The turnkey concept was a game changer for India and became such an entrenched business model that it was adopted by global competitors such as Gamesa and Vestas and is now the norm for doing business in India.
For the Indian market, Suzlon's second core competence was its manufacturing base. Very early on the company realised the importance of the supply chain and developed a strategy to venture into component manufacturing and strategic acquisitions. Suzlon board members have previously explained that the strategy was put into practice once it became apparent that component supplies were not scaling up fast enough to meet the rapid growth in the demand side of the wind-energy market. This strategy gave Suzlon a huge competitive edge by enabling it to meet the growing demand that its competitors were ill-equipped to supply.
Suzlon has now been the market leader in India for 14 years, with a 43% share of all installed turbines and total installed capacity of about 7.4GW. Its customers, who in the main buy the finished wind projects, range from small businesses to large public-sector organisations and some of the country's large independent power producers, including Mytrah Energy and Tata Power.
Under the turnkey concept of project development, Suzlon offers a ready-made wind farm to investors. This requires it to be involved in all aspects of development, including land prospecting, wind resource monitoring, land acquisition and development, supply, construction and commissioning and, finally, operation and maintenance support (see graphic). Suzlon also provides assistance for loan processing and liaisons with utilities on behalf of its customers.
In this way, Suzlon can sell a service rather than a product, which is particularly attractive for small businesses as it allows them to participate in the investment process without building any competence in the sector. From the developer's perspective, this model enables Suzlon to share costs across larger facilities and reduce unit infrastructure costs. It is for these reasons that the turnkey model of business has flourished in India. It has also led to Suzlon's dominance of the Indian market. With strong roots in all major wind states (see map), Suzlon is expected to continue to dominate the market at the state and national level.
However, in terms of annual capacity addition, despite a staggering 1GW of installations in the financial year 2011/12, Suzlon's actual market share in the annual installed capacity amounts to only 37%. While this may be on account of temporary problems in project execution or unanticipated delays in site approvals, the low share could also mean that new competitors are gaining ground in the growing market.
Many new entrants and entrenched players have introduced new low wind speed turbines (IEC class III), which are expected to perform better in the Indian wind environment. In contrast, the most successful Suzlon models in India have so far been class II machines. This has prompted Suzlon to develop new and improved designs for low wind regime conditions.
Product challenge
In April last year, Suzlon launched a range of 2.1MW machines with hub heights at 80, 90 and 100 metres, allowing higher power generation and greater cost benefits in lowto medium-wind speeds. As well as entering the low-wind speed market, Suzlon is using doubly-fed induction generator technology for the first time. It is also working on another class III turbine, a 2.1MW machine with 111 metre rotor diameter, which is expected to increase output by 29%.
Given Suzlon's' dominance in India's market, these new models are expected to up the ante by transforming the way turbine technologies are deployed.
Suhas Tendular is deputy general manager of projects at the World institute of Sustainable Energy.
India has the potential to stay at the top
Suzlon turns to CDR over $220 million debt
Suzlon fails to extend $220 million deadline
Suzlon-developed projects and top owners
Company news & reports

redpajama_set_name: RedPajamaCommonCrawl


text:
A Game-Changing Force Of Will
It's hard to overstate the impact of Jesse Shofner on Oregon Fugue and the entire college division -- or to underestimate her potential impact now that she's joined Scandal.
August 31, 2016 by Robert Gough in Profile with 0 comments
Oregon's Jesse Shofner. Photo: Paul Rutherford — UltiPhotos.com
This is Jesse Shofner. A notorious play-maker, a momentum-shifter, a game-changing force of will. She is known as a fire-cracker, intensity and energy bubbling just beneath the surface as she runs teams into the ground, barely containing her passion and excitement with calculated post-goal spikes. You can see it in her eyes after a score: the competitive nature, the fight to win, the desire to be great.
Jesse's talents and dedication to hard work have allowed her to become a long-time local, national, and international winner. She first competed at Club Nationals after just one year of high school ultimate. She was an integral part of the winningest college team in nearly a decade, reaching semifinals at College Nationals in each of her five years with Oregon Fugue, winning the whole thing twice. She has represented her country in multiple international events and has recently been a part of two of the most unique and exciting mishmash groups of the country's elite players: the 2015 and 2016 All-Star Ultimate Tour and last fall's Team E.R.I.C. Manila Spirits team.
But these are the things you likely already know about Jesse. How did she get here? Where did she start? And the most intriguing question of all, where does she go from here?
Jesse got her start with ultimate in her front yard in Nashville, Tennessee alongside her brother Markham, whose style might look familiar. When she was an 8th grader, Markham started taking his sister to the practices of the high school team, named Brutal Grassburn. Jesse would go on to play four years with the team, often marking up against boys, since female players were few and far between at the time. This first team is where Jesse got hooked on the sport.
"Throughout that [first] year," Jesse recalls, "I learned who Paideia and Amherst were, how awesome field snacks are, and how to rock the go-to youth frisbee girl look: french braids, backwards trucker hat, soccer socks, and baggy shorts."
In addition to the fashion, this is also where Jesse's playing style started to come together. "While playing for Brutal Grassburn, I was taught how to grit, grind, and fearlessly play with and against the boys by some awesome women: Nora Hoover, Margie Quinn, Allyson Lutz, and Tobey Beaver — often wearing what I would later learn was an Ego jersey."
Even those beyond Tennessee quickly took notice of the precocious talent. "Shof's defining traits — her explosiveness and fearlessness — have been there since she was 14," says Washington Element's coach, Kyle Weisbrod, who has coached against Shofner for eight of the past nine years.
In 2008, after just one season at the high-school level, Shofner joined up with a new mixed team named Guillermo y Compania (GYC for short) alongside her brother and earned a bid to Club Nationals at the age of 15.
"I've had two monumental years of growth as a player," she explains. "One, common to most people, being my freshman year of college; the other being that season with GYC. Let's put it this way: I entered that season wearing basketball shorts and came out wearing Patagonia; I learned what cushion defense was (from Yusi Turrell, an amazing handler who clocked years with Lady Godiva); and I learned what a break was, how to get one, and that I wanted more of them."
For the next two years, Jesse played with the local women's team, DeSoto, and would eventually try out for the US's U20 National team that would compete at the 2010 World Junior Ultimate Championships. She wouldn't make the roster.1 She did, however, meet an important friend in the process.
"During the lunch break, I met a girl while sitting on a rock wall — Paideia and Amherst were busy eating with each other, and I didn't know anyone — and we shared a cookie. Her name was Qxhna Titcomb."
Qxhna would later put together the All-Star Ultimate Tour, which Shofner joined in its inaugural year in 2015; she was one of only five players to reprise the role this summer.
Go-Go-Go All The Time
Jesse started college at the University of Oregon in 2012.
"She was pretty raw when she got to Oregon," recalls long-time Oregon Fugue coach Lou Burruss. "She began as a defender and moved over to offense as her game matured."
And oh, how her play has matured. Most players on high-level teams have specific roles and positions carved out for them that align with their skills and physical abilities. Obviously, there are players who can succeed doing multiple things, but few, if any, have been able to play at any position or role one can think of and succeed in the way Shofner can and has.
When asked how this was possible, Burruss answered, "Mostly, it's because she's really good." But more specifically, he says, "on (defense) her tenacity and on (offense) her cutting speed — she learned early to cut 100% all the time. And on both sides of the disc, it helps that she is go-go-go all the time."
"She's dynamic, creative and not bound by what many think of as the 'conventional rules' of ultimate strategy," comments Weisbrod. "Shof doesn't shy away from the hard work, the toughest match-ups, the most difficult plays, or the spotlight."
That last fact turned out to be the most important one. If you are willing to work harder than everyone else, you give yourself the opportunity to compete at any position. And that's just what Jesse has done.
"She's done a little bit of everything, except deep-deep," Burruss explained. "One of her really great skills is that she isn't one-dimensional and that has really allowed us to move her around and have her play in different spots. The team has also changed a lot over the years she has been on it, so what we've needed her to do has changed a lot as well."
One of the reasons Shofner had the freedom to play different roles during her time at Oregon was the presence of teammates Bethany Kaylor and Alex Ode. These three players are each remarkably talented, each natural athletes, and each super versatile players that can play on either side of the disc.
"They each have a unique style," claims Burruss, "but it was really easy to build a team with those three because you can put them anywhere and they will do good."
"I wouldn't be the player I am today without these two," Shofner concludes. "Alex, Beth, and I all came onto Fugue together and have experienced an interesting dynamic. We are really close, good friends, but we are also very competitive… Because of this inherent competition between us — [and] undying honesty and support and friendship — we have played every practice of the last five years busting our asses trying to be the best; we've always relentlessly pushed each other."
Lou Burruss & Jesse Shofner at 2014 Nationals. Photo: Kevin Leclaire — UltiPhotos.com
Together, they earned two national championships, two second-place finishes, and another semi-finals appearance in their time at Oregon. In 2016, Shofner was named Ultiworld's College Player of the Year and finished second in the Callahan voting.
There is a long list of choices and chances that finds a player like Shofner at a school like Oregon with a (now former) coach like Burruss. It's impossible to know what type of player Shofner would be today without her brother Markham, her classmate Dylan Freechild2, her teammates on Fugue, and her college coach. Most have said that Jesse has had the fire and energy forever, which is likely true, but there's no doubt that this is one of the most perfect marriages of a player to a system that we've ever seen.
From One System to Another
As the spring campaign came to a close for one of the most successful and effective players in the history of college ultimate, Jesse surprised us all by initially deciding to sit out the 2016 club season.
While a handful of factors likely led to such a choice, including a chance for some free-time away from the sport for the first time in years, the semifinals exit in her final College Nationals appearance was certainly a punch in the gut — "heartbroken" was a word Shofner used in conversation. In fact, she was only a last-minute addition to the 2016 All-Star Tour, an honor that was likely hers the entire way if she wanted it, which she wasn't sure she did at first.
After some deliberation, Jesse joined the Tour, and we got a few more precious weeks of watching the electrifying show that is Jesse Shofner playing ultimate.
While not committing to the club season, Jesse did have plans for the near future; mostly creating art and eating Oreos. She'd work on her portfolio as a Digital Arts graduate, something that is hard to do when you are practicing and competing at elite-level college ultimate during the spring. She'll finish her Theater Arts minor in the fall after earning several more credits — Kaylor boasts that Jesse is "a great artist and actress; in addition to [frisbee], she brings dedication to both of these realms." She'd even travel the country with her mother, who has a new middle-school book, while applying for jobs.
Now that her USAU college eligibility has wrapped and her schooling is (mostly) done, Jesse is free from her biggest geographical ties. Long-time Fugue and Schwa teammate Bethany Kaylor also graduated, and took a lovely offer and opportunity to move to the Bay Area, where she is playing with San Francisco's Nightlock. Alex Ode headed north to Seattle and joined Riot. Both played for Oregon-based Schwa in previous years, but both chose to explore new options after schooling concluded.
Shofner decided to explore an even different route: not playing ultimate, even in her home state. That is, until she found herself on the sidelines of the 2016 Colorado Cup, talking with D.C. Scandal's Jenny Fey.
"Jenny Fey said 'Come play with me'," Shofner remembers. It was Saturday, the first day of the tournament. A day later, her brother Markham — who Jenny Fey says is "one of her closest pals in DC" — asked her if she had talked to Scandal head coach Alex "Dutchy" Ghesquiere yet. "Is this for real?" Jesse asked. Markham said yes.
The two failed to connect in Denver, so the conversation was bookmarked until the All-Star Tour came to Washington D.C. a week later for their exhibition match.
Shofner described her conversation with Ghesquiere: "After the game, Dutchy and I talked about what it would look like: what role he envisioned for me and what team events they would expect me at. I asked about team philosophies and principles… I asked about team culture." It sounded good. That same night, Jesse spent time with some Scandal players: "They stole my heart when they whipped out Hamilton songs in the middle of a living room."
Another silver lining in the offer was the lighter time commitment; after planning to use the club season for other productive purposes, this route would still leave Shofner a good portion of time to do those things. Ghesquiere told Jesse he would ask the team to confirm their stance on picking up Shofner for the rest of the season. He would get back to her a few days later, and Jesse would discuss the opportunity with her brother Markham, her mother, Fey, and a couple of friends. After some more deliberation, Jesse accepted the offer to join the team.
After wild success in a clear, direct system at Oregon, Shofner now joins another vaunted system under coach Ghesquiere.
"Honestly, I don't know much about this team," Shofner confesses. "I do know that they mean business. I know that I love Jenny Fey, the way she plays and views the game, and I can't wait to step on the field with her. Speaking of can't wait, I can't wait to throw to Sandy (Jorgensen). I'm definitely looking forward to being coached by Dutchy . . . All of Scandal has been very welcoming and they seem like a fun bunch."
The early returns are really promising. Shofner slotted seamlessly into the Scandal squad at Pro Flight Finale and helped the team achieve their best result of the 2016 season — at least so far. With the kind of impact Jesse is capable of bringing on the field, Scandal's ceiling just got a whole lot higher entering the Club Series and they have put the rest of the division on notice that they will again be a force to be reckoned with in Rockford.
Regardless of what else the future holds, one thing is certain: Fugue will always be a part of Jesse Shofner. When asked what the impact of the team has had on her as a player and a person, she replied with one simple word:
Jesse continued: "There is a level of discourse about playing and living that I have never been exposed to quite like this. It helps that the people I have had the privilege to call teammates are intelligent and thoughtful… It's unavoidable that this team has become my family."
Lou Burruss' "Clown Tent" philosophy serves as a base for the team's dynamics and relationships, but it is a fluid, ever-changing idea. What you'll hear most commonly from the Fugue players are the ideas of being accepting of everyone, being supportive of everyone, learning to lead, learn, and grow among peers, and much more.
"It will be a sad day when I can no longer take the field with Fugue," Jesse admitted a couple months ago. "But being a part of Fugue never goes away. Perhaps more importantly, Fugue being a part of me will never go away."
Like her relationship with her college team, Jesse seems destined to always be a part of collegiate ultimate. Her talents and abilities, her spirit, her results, her highlights — all of it will all stay with us as future players mimic her moves, as coaches mimic her system's successes, as well as her own. We'll be reminded of her with every new fast-paced player that graces the ultimate field, until some new generational talent comes along to challenge Shofner's place in the "all-time college greats" discussions.
But I don't see that happening anytime soon.
Art credit: Jesse Shofner
Shofner would eventually get her chance to compete on the international stage, earning silver medals on the 2012 U20 team and the 2015 U23 team. ↩
whose playing style is clearly in the same vein and with whom Shofner would share advice during their time together at Oregon ↩
Robert Gough
Robert is an ultimate player/blogger based in Portland, OR playing for Portland's premiere Dungeons and Dragons themed mixed team, the Natural 20's. You can contact Robert on twitter (@bobbygough) or email (rob.p.gough@gmail.com).
TAGGED: USAU Club Women's, USAU College D-I Women's, Alex Ghesquiere, Alex Ode, All Star Ultimate Tour, Bethany Kaylor, Jenny Fey, Jesse Shofner, Lou Burruss, U23
TEAMS: Oregon, Washington DC Scandal
The 2016 Ultiworld College Women's Player Of The Year, Presented By Nike Ultimate Camps
Seattle Riot 2016 Roster
12 Days of College Ultimate: Two First-Time HBCU Teams
Coach Speak: National Team Coaching with Alex Ghesquiere and Ben Van Heuvelen
Ditch, The New Media Company From NexGen/E.R.I.C. Founders, Goes Live
Universe Point: Molly Brown vs. Brute Squad
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text:
Q: Selenium with Python on ssh server: InvalidElementStateException & ElementNotVisibleException I'm trying to log in into Quora website.
 On my local machine it runs perfectly.
But on SSH server (droplet on DigitalOcean) - no, I'm getting InvalidElementStateException
I've tried to focus on the element by send_keys(Keys.NULL), and got  ElementNotVisibleException
Here is the code:
driver.get("https://www.quora.com/")
print("Logging...")

# gets email and password from json
with open('config.json') as f:
    login_data = json.load(f)
email = login_data['email']
password = login_data['pass']
time.sleep(3)
email_field_xpath = "//div[@class='form_column']/input[@name='email']"
password_field_xpath = "//div[@class='form_column']/input[@name='password']"

# webdriver's going to wait max 10 seconds for email's field, password field, login button to display
email_field_element = WebDriverWait(driver, 10).until(
    lambda driver: driver.find_element_by_xpath(email_field_xpath))
password_field_element = WebDriverWait(driver, 10).until(
    lambda driver: driver.find_element_by_xpath(password_field_xpath))

email_field_element.send_keys(Keys.NULL)
email_field_element.clear()
email_field_element.send_keys(email)
password_field_element.send_keys(Keys.NULL)
password_field_element.clear()
password_field_element.send_keys(password)
login_button_xpath = "//input[@value='Login']"
# wait till element is clickable
login_button_element = WebDriverWait(driver, 10).until(
    EC.element_to_be_clickable((By.XPATH, login_button_xpath)))
login_button_element.click()
print("Logged In.")

A: It may possible that screen resolution you are using at your local machine and on SSH server are different. May be the size of opened browser windows are different at both at local and on SSH server.
Try to change your local settings as SSH server's than you can figure out what is actual reason or just try the possible solutions given below.
Possible solutions by focusing on element first. Here, your element can be email_field_element or password_field_element:
1) First move to that element using ActionChains:
from selenium.webdriver.common.action_chains import ActionChains
actions = ActionChains(driver)
actions.move_to_element(element)
actions.perform()

OR
2) You can also use scroll up to that element. Like:
driver.execute_script("arguments[0].scrollIntoView(true);", element)

OR
3)Input data using java script.
driver.execute_script("document.getElementById('elementID').setAttribute('value', 'new value for element')" # you can use xpath as well

Hope it may help you.

redpajama_set_name: RedPajamaStackExchange


Robert Mood received the Fritt Ord Honorary Award for his fearless openness as the head of the Norwegian Armed Forces. In this book, he writes about leadership as a form of defense for him; first and foremost, people and their values and opinions aren't weapons or vessels. A leader must have enough integrity and security to stand for his values, no matter which way the political winds blow.


In this study, I introduce a parsimonious model that explains implied volatility time series for individual stock options. The current state of risk management for individual equity options still seems to lack the presence of pertinent exogenous variables. This study suggests a few easily observable variables that can be used to explain the changes in implied volatilities of stock options. These variables can be used in the risk models in order to more accurately manage option positions for individual stocks. The first chapter provides a motivation for the VIX as the primary explanatory variable for changes in implied volatility. It also examines the role of fundamental variables. The second chapter shows that the VIX as a good explanatory variable for explaining changes in implied volatility. It also examines the return of the underlying asset as an explanatory variable. Various techniques are used to determine the efficacy of the variables such as Fama-Macbeth cross-sectional regressions, Principal Component analysis, and individual regressions for each company in the sample. The final chapter examines risk premia in straddle returns and provides a practical application of volatility hedging.


text:
Creative specializing in product design and strategy in New York.
I have worked with folks such as Splice, Tumblr, NBC and Telemundo, Brand Foundry Ventures, as well as musicians and artists.
I'm currently working on the product design team at Grailed.
See my work
Born and raised in Washington, DC, I moved to NY in 2003 to study design at Brooklyn's Pratt Institute. I loved New York so much I stayed.
In the early aughts, I started a podcast that eventually turned into a record label called Subdrive. Since then it's had releases and artists supported by Diplo, Sasha, Township Rebellion, Damian Lazarus, BBC Radio 1, and charted on Beatport.
Skills & Hocus Pocus
Fluent in Spanish (Read, Write, Speak)
UX Research
Process and Planning
Agile Experience
Product and Design Strategy
UI/UX Product Design
Software & Powers
Microsoft Office Suite
Pratt Institute - Brooklyn, NY
Bachelor of Fine Arts, Communications Design 2003 – 2007
© 2021 Ricardo Galbis
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