text:
\section*{Acknowledgements}

We thank anonymous reviewers for their helpful comments. Thanks to Yibo Sun, Tiantian Jiang, Daxing Zhang, Rongtian Bian, Heng Zhang, Hu Zhenyu and other students of Harbin Institute of Technology for their support in the process of data set annotation. Thanks to Wenpeng Hu, a Ph.D. student of Peking University, for providing preprocessed Ubuntu data. The research in this article is supported by the Science and Technology Innovation 2030 - "New Generation Artificial Intelligence" Major Project (2018AA0101901), the National Key Research and Development Project (2018YFB1005103), the National Science Foundation of China (61772156, 61976073) and the Foundation of Heilongjiang Province (F2018013).

\section{Conclusion}

In this paper, we introduce Molweni, a multiparty dialog dataset for machine reading comprehension (MRC). Compared with traditional textual structure, the dialog is concatenated by the utterances from multiple participants. We believe that discourse structure can provide potential help for understanding the dialog.  Therefore, we ask annotators to label the discourse dependency structure of the multiparty dialog and propose questions for the dialog. Annotation on a large number of dialog shows that tagging discourse structure can significantly help taggers understand dialog and raise higher quality questions. In the future, we will try to propose novel discourse parsing models for multiparty dialog and apply discourse structure in the reading comprehension task of multiparty dialog.

\section{Experiments}

We now introduce our baseline experiments on our dataset. We consider the two tasks of discourse parsing and machine comprehension for multiparty dialog.

\subsection{Machine reading comprehension for multiparty dialogs}

\paragraph{Methods.} SQuAD~2.0 is an MRC dataset that adopts a passage as the input and the answer is a span from input passage \cite{rajpurkar2018know}.  We adopt the following existing methods for SQuAD~2.0 on our dataset. In this paper, we use three different kinds of settings of BERT: BERT-base, BERT-large, and BERT-whole word masking (BERT-wwm).
We concatenate all utterances from input dialog as a passage, and each utterance includes speaker and text.
We used the open-source code of BERT to perform our experiments\footnote{\url{https://github.com/google-research/bert}}.

BERT is a bidirectional encoder from  transformers \cite{devlin2019bert}. To learn better representations for text, BERT adopts two objectives: masked language modeling and the next sentence prediction during pretraining.  In the BERT-wwm, if a part of a complete word WordPiece is replaced by [mask], the other parts of the same word will also be replaced by mask, which is the whole word mask.

\begin{itemize}
{

    \item \textbf{BERT-base:} 12-layer, 768-hidden, 12-heads, 110M parameters.
    \item \textbf{BERT-large:} 24-layer, 1024-hidden, 16-heads, 340M parameters. The difference between BERT-base and BERT-large is in the number of the parameters; there is no difference in model architecture.
    \item \textbf{BERT-wwm:} 24-layer, 1024-hidden, 16-heads, 340M parameters. The original word segmentation method based on WordPiece segments a complete word into several affixes. When generating training samples, these separated affixes are randomly replaced by {\tt [mask]}.

}
\end{itemize}

\input{mrc_results.tex}

\paragraph{Evaluation Metric.} As our task is quite related to SQuAD~2.0, we adopt the same evaluation metrics: exact match (EM) and $F_1$ score to evaluate experiments. EM measures the percentage of predictions that match all words of the ground truth answers exactly. $F_1$ scores are a looser interpretation of match, measuring the average overlap between predictions and the ground truth answer. The results of machine reading comprehension for multiparty dialogs is shown in Table~7.

\paragraph{Human upper bound.} We enlist two non-annotator volunteers whose majors are computer science to answer questions in the {\sc Test} set.  From Table~7, they achieved 64.3\% in EM and 80.2\% in $F_1$. This result show that (1) People can get good results in $F_1$, and (2) it is challenging to detect the accurate boundary of answers.
The results of humans show the challenge of machine comprehension for multiparty dialogs because the structure of a multiparty dialog is very complex and the language style in dialogs is very informal compared with well-written passage text.

\paragraph{Results.} For three BERT models, the BERT-wwm model achieves the best results on both SQuAD~2.0 and our Molweni dataset, followed by BERT-large and BERT-base. Especially, the BERT-wwm model gets 89.1\% $F_1$ score on SQuAD~2.0, very close to human performance.  The performance gap between BERT-wwm and human are 0.1\% EM and 0.3\% $F_1$ on SQuAD~2.0. However, on Molweni, BERT-wwm achieves only 67.7\% $F_1$, which has a significant large 12.5\% performance gap with human performance.

\paragraph{Case study}In this part, we will analyze the reason why BERT-wwm does not perform as well as it does on SQuAD~2.0. Fig.4 shows an example of dialog~3 in our Molweni test set with two bad cases of the BERT-wwm model. In Dialog~3, there are three speakers and ten utterances. The first question Q1 is about the user that asked for the address. The answer to BERT-wwm of Q1 is \textit{likwidoxigen}, but the gold answer is \textit{nbx909}. The second question Q2 is about the status of printers, but the model answers the status of people who makes the printers.

\begin{figure}
    \centering
    \includegraphics[width=\textwidth]{dialogue3.pdf}

    \caption{Dialogue3. (a) A real example from Molweni dataset with three speakers and ten utterances. (b) Two questions for Dialog~3 and the pridected answers of BERT-wwm model.}
    \label{fig:framework}
\end{figure}

We concatenate all utterances as the input which doesn't highlight the speaker information of the utterance. For Q1, after concatenating all utterances, \textit{likwidoxigen} would be the closest speaker in the input with the word 'address'. The speaker of utterances is the essential information for better understanding dialogs.

On the other hand, when concatenating all utterances, the language model could automatically model the coherence between two adjacent utterances. But there could be no coherence between adjacent utterances, and the discourse structure of a multiparty dialog should not be regarded as a sequence but a graph. In most cases, every node (utterance) in the discourse dependency graph only has one parent node.

\subsection{Discourse parsing for multiparty dialogs}

\paragraph{Methods} We perform the Deep Sequential model on our Molweni corpus which is the state-of-the-art model on STAC. \newcite{shi2019deep} proposed the deep sequential model for discourse parsing on multiparty chat dialogs which adopted an iterative algorithm to learn the structured representation and highlight the speaker information in the dialog. The model jointly and alternately learns the dependency structure and discourse relations.

In this paper, we adopt two different kinds of the setting of the Deep Sequential model.
\begin{itemize}
{

    \item \textbf{Deep sequential} This is the original deep sequential model.
    \item \textbf{Deep sequential(C)}  Considering that we adopt the same discourse relation hierarchy with the STAC corpus, we combine the training sets of STAC and Molweni as the training set for this model, we respectively test the model on STAC and Molweni.
}
\end{itemize}

\paragraph{Results} We adopt the F1 score to evaluate both links prediction and relation classification tasks, which is the same as previous literature. The results of discourse parsing for multiparty dialogs are shown in Table~8. For link prediction, we achieved higher results than the deep sequential model performed on STAC. On the other hand, we achieve comparable results for relations classification compared with STAC. After combining the training set of Molweni, the deep sequential model achieves better results on STAC which means the Molweni dataset can be beneficial to predict discourse dependency links.

\input{dp_results.tex}

\section{Introduction}

Research into multiparty dialog has recently grown considerably, partially due to the growing ubiquity of dialog agents. Multiparty dialog applications such as discourse parsing and meeting summarization are now mainstream research \cite{shi2019deep,GSNijcai2019,li2019keep,zhao2019abstractive,sun2019dream,N16IntegerLP,D15DP4MultiPparty}. Such applications must consider the more complex, graphical nature of discourse structure: coherence between adjacent utterances is not a given, unlike standard prose where sequential guarantees hold.

In a separate vein, the area of machine reading comprehension (MRC) research has also made unbridled progress recently.  Most existing datasets for machine reading comprehension (MRC) adopt well-written prose passages and historical questions as inputs \cite{richardson2013mctest,rajpurkar2016squad,lai2017race,choi2018quac,reddy2019coqa}.

Reading comprehension for dialog --- as the intersection of these two areas --- has naturally begun to attract interest.
\newcite{ma2018challenging} constructed a small dataset for passage completion on multiparty dialog, but which has been easily dispatched by CNN+LSTM models using attention. The DREAM corpus \cite{sun2019dream} is an MRC dataset for dialog, but only features a minute fraction (1\%) of multiparty dialog. FriendsQA is a small-scale span-based MRC dataset for multiparty dialog, which derives from TV show \textit{Friends}, including 1,222 dialogs and 10,610 questions \cite{yang-choi-2019-friendsqa}.
The limited number of dialogs in FriendsQA makes it infeasible to train more complex
model to represent multiparty dialogs due to overfitting, and the lack of annotated discourse structure prevents models from making full use of the characteristics of multiparty dialog.

Dialog-based MRC thus varies from other MRC variants in two key aspects:
\begin{enumerate}
	\item[C1.] Utterances of multiparty dialog are much less locally coherent than in prose passages.  A passage is a continuous text where there is a discourse relation between every two adjacent sentences. Therefore, we can regard each paragraph in a passage as a linear discourse structure text. In contrast, there may be no discourse relation between adjacent utterances in a multiparty dialog. As such, we regard the discourse structure of a multiparty dialog  as a dependency graph where each node is an utterance.

	\item[C2.] Multiparty dialog subsumes the special case of two-party dialog.

	In most cases, the discourse structure of a two-party dialog is tree-like, where discourse relations mostly occur between adjacent utterances. However, in multiparty dialog, such assumptions hold less often as two utterances may participate in discourse relations, though they are very distant.
\end{enumerate}

\begin{figure}
	\centering
	\includegraphics[width=\textwidth]{multi.pdf}

	\caption{(Dialog~1) A corpus example from Molweni. There are four speakers in the dialog: \textit{nbx909}, \textit{Likwidoxigen}, \textit{babo}, and \textit{nuked}. In total, the speakers make seven utterances: $U_1$ to $U_7$. Our annotators proposed three questions against the provided dialog: Q1--3, where Q1 and Q2 are answerable questions, and Q3 is unanswerable. Due to the properties of informal dialog, the instances in our corpus often have grammatical errors.}
	\label{fig:framework}
\end{figure}

Prior MRC works do not consider the properties of multiparty dialog. To address this gap in understanding of multiparty dialog, we created Molweni. In Dialog~1 (\textit{cf.} Fig~1), four speakers converse over seven utterances.  We additionally employ annotators to read the passage and contribute questions: in the example, the annotators propose three questions: two answerable and one unanswerable.  We observe that adjacent utterance pairs can be incoherent, illustrating the key challenge.  It is non-trivial to detect discourse relations between non-adjacent utterances; and crucially, difficult to correctly interpret a multiparty dialog without a proper understanding of the input's complex structure.

We derived Molweni from the large-scale multiparty dialog Ubuntu Chat Corpus \cite{lowe2015ubuntu}.  We chose the name {\it Molweni}, as it is the plural form of ``Hello'' in the Xhosa language, representing multiparty dialog in the same language as {\it Ubuntu}. Our dataset contains 10,000 dialogs with 88,303 utterances and 30,066 questions including answerable and unanswerable questions. All answerable questions are extractive questions whose answer is a span in the source dialog. For unanswerable questions, we annotate their plausible answers from dialog. Most questions in Molweni are 5W1H questions -- {\it Why}, {\it What}, {\it Who}, {\it Where}, {\it When}, and {\it How}. For each dialog in the corpus, annotators propose three questions and find the answer span (if answerable) in the input dialog.

To assess the difficulty of Molweni as an MRC corpus, we train BERT's whole word masking model on Molweni, achieving a 54.7\% exact match (EM) and 67.7\% $F_1$ scores. Both scores show larger than 10\% gap with
human performance, validating its difficulty. Due to the complex structure of multiparty dialog,  human performance just achieves 80.2\% $F_1$ on Molweni.
In particular, annotators agreed that knowledge of the correct discourse structure would be helpful for systems to achieve better MRC performance.

This comes to the second key contribution of Molweni.  We further annotated all 78,245 discourse relations in all of Molweni's dialogs, in light of the potential help that annotated discourse structure might serve.  Prior to Molweni, the STAC corpus is the only dataset for multiparty dialog discourse parsing \cite{asher2016discourse}.  However, its limited scale (only 1K dialogs) disallow data-driven approaches to discourse parsing for multiparty dialog. We saw the additional opportunity to empower and drive this direction of research for multiparty dialog processing.

\section*{Acknowledgements}

We thank anonymous reviewers for their helpful comments. Thanks to Yibo Sun, Tiantian Jiang, Daxing Zhang, Rongtian Bian, Heng Zhang, Hu Zhenyu and other students of Harbin Institute of Technology for their support in the process of data set annotation. Thanks to Wenpeng Hu, a Ph.D. student of Peking University, for providing preprocessed Ubuntu data. The research in this article is supported by the Science and Technology Innovation 2030 - "New Generation Artificial Intelligence" Major Project (2018AA0101901), the National Key Research and Development Project (2018YFB1005103), the National Science Foundation of China (61772156, 61976073) and the Foundation of Heilongjiang Province (F2018013).

\section{Conclusion}

In this paper, we introduce Molweni, a multiparty dialog dataset for machine reading comprehension (MRC). Compared with traditional textual structure, the dialog is concatenated by the utterances from multiple participants. We believe that discourse structure can provide potential help for understanding the dialog.  Therefore, we ask annotators to label the discourse dependency structure of the multiparty dialog and propose questions for the dialog. Annotation on a large number of dialog shows that tagging discourse structure can significantly help taggers understand dialog and raise higher quality questions. In the future, we will try to propose novel discourse parsing models for multiparty dialog and apply discourse structure in the reading comprehension task of multiparty dialog.

\section{Experiments}

We now introduce our baseline experiments on our dataset. We consider the two tasks of discourse parsing and machine comprehension for multiparty dialog.

\subsection{Machine reading comprehension for multiparty dialogs}

\paragraph{Methods.} SQuAD~2.0 is an MRC dataset that adopts a passage as the input and the answer is a span from input passage \cite{rajpurkar2018know}.  We adopt the following existing methods for SQuAD~2.0 on our dataset. In this paper, we use three different kinds of settings of BERT: BERT-base, BERT-large, and BERT-whole word masking (BERT-wwm).
We concatenate all utterances from input dialog as a passage, and each utterance includes speaker and text.
We used the open-source code of BERT to perform our experiments\footnote{\url{https://github.com/google-research/bert}}.

BERT is a bidirectional encoder from  transformers \cite{devlin2019bert}. To learn better representations for text, BERT adopts two objectives: masked language modeling and the next sentence prediction during pretraining.  In the BERT-wwm, if a part of a complete word WordPiece is replaced by [mask], the other parts of the same word will also be replaced by mask, which is the whole word mask.

\begin{itemize}
{

    \item \textbf{BERT-base:} 12-layer, 768-hidden, 12-heads, 110M parameters.
    \item \textbf{BERT-large:} 24-layer, 1024-hidden, 16-heads, 340M parameters. The difference between BERT-base and BERT-large is in the number of the parameters; there is no difference in model architecture.
    \item \textbf{BERT-wwm:} 24-layer, 1024-hidden, 16-heads, 340M parameters. The original word segmentation method based on WordPiece segments a complete word into several affixes. When generating training samples, these separated affixes are randomly replaced by {\tt [mask]}.

}
\end{itemize}

\input{mrc_results.tex}

\paragraph{Evaluation Metric.} As our task is quite related to SQuAD~2.0, we adopt the same evaluation metrics: exact match (EM) and $F_1$ score to evaluate experiments. EM measures the percentage of predictions that match all words of the ground truth answers exactly. $F_1$ scores are a looser interpretation of match, measuring the average overlap between predictions and the ground truth answer. The results of machine reading comprehension for multiparty dialogs is shown in Table~7.

\paragraph{Human upper bound.} We enlist two non-annotator volunteers whose majors are computer science to answer questions in the {\sc Test} set.  From Table~7, they achieved 64.3\% in EM and 80.2\% in $F_1$. This result show that (1) People can get good results in $F_1$, and (2) it is challenging to detect the accurate boundary of answers.
The results of humans show the challenge of machine comprehension for multiparty dialogs because the structure of a multiparty dialog is very complex and the language style in dialogs is very informal compared with well-written passage text.

\paragraph{Results.} For three BERT models, the BERT-wwm model achieves the best results on both SQuAD~2.0 and our Molweni dataset, followed by BERT-large and BERT-base. Especially, the BERT-wwm model gets 89.1\% $F_1$ score on SQuAD~2.0, very close to human performance.  The performance gap between BERT-wwm and human are 0.1\% EM and 0.3\% $F_1$ on SQuAD~2.0. However, on Molweni, BERT-wwm achieves only 67.7\% $F_1$, which has a significant large 12.5\% performance gap with human performance.

\paragraph{Case study}In this part, we will analyze the reason why BERT-wwm does not perform as well as it does on SQuAD~2.0. Fig.4 shows an example of dialog~3 in our Molweni test set with two bad cases of the BERT-wwm model. In Dialog~3, there are three speakers and ten utterances. The first question Q1 is about the user that asked for the address. The answer to BERT-wwm of Q1 is \textit{likwidoxigen}, but the gold answer is \textit{nbx909}. The second question Q2 is about the status of printers, but the model answers the status of people who makes the printers.

\begin{figure}
    \centering
    \includegraphics[width=\textwidth]{dialogue3.pdf}

    \caption{Dialogue3. (a) A real example from Molweni dataset with three speakers and ten utterances. (b) Two questions for Dialog~3 and the pridected answers of BERT-wwm model.}
    \label{fig:framework}
\end{figure}

We concatenate all utterances as the input which doesn't highlight the speaker information of the utterance. For Q1, after concatenating all utterances, \textit{likwidoxigen} would be the closest speaker in the input with the word 'address'. The speaker of utterances is the essential information for better understanding dialogs.

On the other hand, when concatenating all utterances, the language model could automatically model the coherence between two adjacent utterances. But there could be no coherence between adjacent utterances, and the discourse structure of a multiparty dialog should not be regarded as a sequence but a graph. In most cases, every node (utterance) in the discourse dependency graph only has one parent node.

\subsection{Discourse parsing for multiparty dialogs}

\paragraph{Methods} We perform the Deep Sequential model on our Molweni corpus which is the state-of-the-art model on STAC. \newcite{shi2019deep} proposed the deep sequential model for discourse parsing on multiparty chat dialogs which adopted an iterative algorithm to learn the structured representation and highlight the speaker information in the dialog. The model jointly and alternately learns the dependency structure and discourse relations.

In this paper, we adopt two different kinds of the setting of the Deep Sequential model.
\begin{itemize}
{

    \item \textbf{Deep sequential} This is the original deep sequential model.
    \item \textbf{Deep sequential(C)}  Considering that we adopt the same discourse relation hierarchy with the STAC corpus, we combine the training sets of STAC and Molweni as the training set for this model, we respectively test the model on STAC and Molweni.
}
\end{itemize}

\paragraph{Results} We adopt the F1 score to evaluate both links prediction and relation classification tasks, which is the same as previous literature. The results of discourse parsing for multiparty dialogs are shown in Table~8. For link prediction, we achieved higher results than the deep sequential model performed on STAC. On the other hand, we achieve comparable results for relations classification compared with STAC. After combining the training set of Molweni, the deep sequential model achieves better results on STAC which means the Molweni dataset can be beneficial to predict discourse dependency links.

\input{dp_results.tex}

\section{Introduction}

Research into multiparty dialog has recently grown considerably, partially due to the growing ubiquity of dialog agents. Multiparty dialog applications such as discourse parsing and meeting summarization are now mainstream research \cite{shi2019deep,GSNijcai2019,li2019keep,zhao2019abstractive,sun2019dream,N16IntegerLP,D15DP4MultiPparty}. Such applications must consider the more complex, graphical nature of discourse structure: coherence between adjacent utterances is not a given, unlike standard prose where sequential guarantees hold.

In a separate vein, the area of machine reading comprehension (MRC) research has also made unbridled progress recently.  Most existing datasets for machine reading comprehension (MRC) adopt well-written prose passages and historical questions as inputs \cite{richardson2013mctest,rajpurkar2016squad,lai2017race,choi2018quac,reddy2019coqa}.

Reading comprehension for dialog --- as the intersection of these two areas --- has naturally begun to attract interest.
\newcite{ma2018challenging} constructed a small dataset for passage completion on multiparty dialog, but which has been easily dispatched by CNN+LSTM models using attention. The DREAM corpus \cite{sun2019dream} is an MRC dataset for dialog, but only features a minute fraction (1\%) of multiparty dialog. FriendsQA is a small-scale span-based MRC dataset for multiparty dialog, which derives from TV show \textit{Friends}, including 1,222 dialogs and 10,610 questions \cite{yang-choi-2019-friendsqa}.
The limited number of dialogs in FriendsQA makes it infeasible to train more complex
model to represent multiparty dialogs due to overfitting, and the lack of annotated discourse structure prevents models from making full use of the characteristics of multiparty dialog.

Dialog-based MRC thus varies from other MRC variants in two key aspects:
\begin{enumerate}
	\item[C1.] Utterances of multiparty dialog are much less locally coherent than in prose passages.  A passage is a continuous text where there is a discourse relation between every two adjacent sentences. Therefore, we can regard each paragraph in a passage as a linear discourse structure text. In contrast, there may be no discourse relation between adjacent utterances in a multiparty dialog. As such, we regard the discourse structure of a multiparty dialog  as a dependency graph where each node is an utterance.

	\item[C2.] Multiparty dialog subsumes the special case of two-party dialog.

	In most cases, the discourse structure of a two-party dialog is tree-like, where discourse relations mostly occur between adjacent utterances. However, in multiparty dialog, such assumptions hold less often as two utterances may participate in discourse relations, though they are very distant.
\end{enumerate}

\begin{figure}
	\centering
	\includegraphics[width=\textwidth]{multi.pdf}

	\caption{(Dialog~1) A corpus example from Molweni. There are four speakers in the dialog: \textit{nbx909}, \textit{Likwidoxigen}, \textit{babo}, and \textit{nuked}. In total, the speakers make seven utterances: $U_1$ to $U_7$. Our annotators proposed three questions against the provided dialog: Q1--3, where Q1 and Q2 are answerable questions, and Q3 is unanswerable. Due to the properties of informal dialog, the instances in our corpus often have grammatical errors.}
	\label{fig:framework}
\end{figure}

Prior MRC works do not consider the properties of multiparty dialog. To address this gap in understanding of multiparty dialog, we created Molweni. In Dialog~1 (\textit{cf.} Fig~1), four speakers converse over seven utterances.  We additionally employ annotators to read the passage and contribute questions: in the example, the annotators propose three questions: two answerable and one unanswerable.  We observe that adjacent utterance pairs can be incoherent, illustrating the key challenge.  It is non-trivial to detect discourse relations between non-adjacent utterances; and crucially, difficult to correctly interpret a multiparty dialog without a proper understanding of the input's complex structure.

We derived Molweni from the large-scale multiparty dialog Ubuntu Chat Corpus \cite{lowe2015ubuntu}.  We chose the name {\it Molweni}, as it is the plural form of ``Hello'' in the Xhosa language, representing multiparty dialog in the same language as {\it Ubuntu}. Our dataset contains 10,000 dialogs with 88,303 utterances and 30,066 questions including answerable and unanswerable questions. All answerable questions are extractive questions whose answer is a span in the source dialog. For unanswerable questions, we annotate their plausible answers from dialog. Most questions in Molweni are 5W1H questions -- {\it Why}, {\it What}, {\it Who}, {\it Where}, {\it When}, and {\it How}. For each dialog in the corpus, annotators propose three questions and find the answer span (if answerable) in the input dialog.

To assess the difficulty of Molweni as an MRC corpus, we train BERT's whole word masking model on Molweni, achieving a 54.7\% exact match (EM) and 67.7\% $F_1$ scores. Both scores show larger than 10\% gap with
human performance, validating its difficulty. Due to the complex structure of multiparty dialog,  human performance just achieves 80.2\% $F_1$ on Molweni.
In particular, annotators agreed that knowledge of the correct discourse structure would be helpful for systems to achieve better MRC performance.

This comes to the second key contribution of Molweni.  We further annotated all 78,245 discourse relations in all of Molweni's dialogs, in light of the potential help that annotated discourse structure might serve.  Prior to Molweni, the STAC corpus is the only dataset for multiparty dialog discourse parsing \cite{asher2016discourse}.  However, its limited scale (only 1K dialogs) disallow data-driven approaches to discourse parsing for multiparty dialog. We saw the additional opportunity to empower and drive this direction of research for multiparty dialog processing.

\section{The Molweni corpus}

Our dataset derives from the large scale multiparty dialogs dataset --- the Ubuntu Chat Corpus \cite{lowe2015ubuntu}.
We list our three reasons in choosing the Ubuntu Chat Corpus as the base corpus for annotation.
\begin{itemize}
	\item First, the Ubuntu dataset is a large multiparty dataset.

	After filtering the dataset by only retaining all utterances with response relations, there are still over 380K sessions and 1.75M utterances. In each session, there are 3-10 utterances and 2-7 interlocutors.
	\item Second, it is easy to annotate the Ubuntu dataset. The Ubuntu dataset already contains Response-to relations that are discourse relations between different speakers' utterances. For annotating discourse dependencies in dialog, we only need to annotate relations between the same speaker's utterances and the specific sense of discourse relation. Because the length of dialogs in the Ubuntu dataset is not too long, we can easily summarize dialogs and propose some questions for the dialog.
	\item Third, there are many papers doing experiments on the Ubuntu dataset, and the dataset has been widely recognized. For example, \newcite{kummerfeld2019large} proposed a large-scale, offshoot dataset for conversation disentanglement based on the Ubuntu IRC log.

	Also recently, \newcite{GSNijcai2019} also used the Ubuntu Chat Corpus as their dataset for learning dialog graph representation.
\end{itemize}

The discourse dependency structure of each multiparty dialog can be regarded as a discourse dependency graph where each node is an utterance. To learn better graph representation of multiparty dialogs, we filter the Ubuntu Chat Corpus for complex dialogs -- those dialogs with 8--15 utterances and 2--9 speakers.
As multiparty dialog is already intensely complex for the current state of the art, we chose to further simplify in our selection criteria, additionally filtering out dialogs with long utterances (more than 20 words).
Finally, we
randomly chose a subset of $\sim$10,000 dialogs with 88,303 utterances from the Ubuntu dataset.
We give an overview of Molweni's key demographics in Table~1.
\input{overview.tex}

10,000 dialogs are divided into two parts: 100 dialogs in common (public dialog) and 9,900 dialogs for different annotators (private dialog). Each annotator is asked to annotate 1,090 dialogs (990 private dialogs and 100 public dialogs)  in two aspects: machine reading comprehension and discourse structure. All annotators chose to annotate the discourse structure of the dialog, and then propose questions and find answer spans for the dialog.
All annotators agreed that it would be helpful to annotate the MRC task after annotating the discourse structure.

In total, our subjects annotated 9,754 dialogs,
slightly fewer than 10,000 dialogs,
consisting of 88,303 utterances, and contributed 30,066 questions for machine reading comprehension and 78,245 discourse relation annotations.
There are 8,771 dialogs in the demarcated {\sc Train} set for both machine reading comprehension and discourse parsing tasks. 883 dialogs are used for {\sc Dev} set. Each annotator is asked to propose three questions per dialog. There are 100 dialogs in common for all ten annotators, and these 100 dialogs comprise our {\sc Test} set. Each dialog in the training set and develop set has three questions.
Our annotation team proposed a total of 2,871 questions for the 100 dialogs in {\sc Test} sets.

Detailed statistics are shown in Table~2. The average number of speakers per dialog is 3.51, which means that most dialogs are multiparty (as opposed to 2-party) dialogs.
The number of two-party dialogs and multiparty dialogs in our dataset is 2,117 and 7,883, respectively.
In Molweni, the average and maximum length of the selected dialogs are 8.82 and 14 utterances, respectively, and the number of answerable and unanswerable questions are 25,779 and 4,287, respectively.

\input{details}

\input{compare_datasets.tex}

\subsection{Annotation for machine reading comprehension}

We hired ten annotators to construct our Molweni dataset. As the Ubuntu corpus is technical in nature, all annotators are undergraduate students whose major is computer science to annotate the corpus. Annotators are non-native English speakers but who have an English proficiency certificate. They are all familiar with Linux operation system.

Annotators propose three questions for each dialog and annotate the span of answers in the input dialog.
There are two types of questions in our corpus, namely, answerable questions and unanswerable questions:
\begin{enumerate}
	\item \textbf{Answerable questions.} For these questions, the answer is a continuous span from source dialog. Annotators were asked to label answers from input dialog and ensure answers were succinct, without including extraneous text.
	\item \textbf{Unanswerable questions.} To make the reading comprehension task more challenging, we annotate unanswerable questions and their plausible answers (PA). The plausible answers are quite related to unanswerable questions.
\end{enumerate}

We compare Molweni against other datasets in Table~3. We see that existing dialog MRC datasets neither contribute either unanswerable questions, nor annotated discourse structure. Due to the complex structure of multiparty dialogs, we believe that it is essential to adopt the discourse dependency structure for the machine modeling towards multiparty dialog understanding. To the best of our knowledge to date, Molweni is the only MRC dataset that is annotated with discourse structure.

We give example questions from Molweni in Table~4. In particular, most of the questions in our dataset are questions lead by {\it Why}, {\it What}, {\it Who}, {\it Where}, {\it When}, and {\it How}. Only a small proportion of the questions are Other questions; questions lead by words such as {\it Do}, {\it Which}, and {\it Whose}. When annotators propose questions, they are asked to consider the characteristics of multiparty dialogs. For example, for {\it Why} and {\it How} questions, it is essential to know the question--answer pair and the cause--result in the dialog. For {\it How} questions, it is important to understand the role of speakers in order to properly represent the multiparty dialog. As such, {\it Why} and {\it How} often require a deeper understanding of the dialog.

\input{5W1H_questions.tex}

\subsection{Annotation for discourse structure of multiparty dialogs}

The task of discourse parsing for multiparty dialogs is to determine the discourse relations among utterances.  To enable better future modeling of  such multiparty discourse, we represent a multiparty dialog by a directed acyclic graph (DAG). The process of annotating the discourse structure consists of two parts: predicting the links between utterances, and classifying the sense of the resultant discourse relation. Table~5 gives an overview of the statistics for Molweni's discourse parsing annotations.

\input{dp_overview}

An edge between two utterances represents the existence of a discourse dependency relation. The direction of the edge represents the direction of discourse dependency. In this subtask, what annotators need to do is to confirm whether two utterances have a discourse relation. Following the convention in the Penn Discourse Treebank (PDTB) \cite{prasad2008penn}, we term the two utterances as \textit{Arg1} and \textit{Arg2},
sequentially.

\input{relations}

Discourse relations on two non-adjacent utterances are rare in prose but common in multiparty dialogs. When we annotate dialogs, annotators should sequentially read the dialog from its beginning  to its final utterance. For each utterance, annotators need to find at least one parent node from among the previous utterances. We assume that the discourse structure is a connected graph and no utterance is isolated.

After we find the discourse relation between two utterances, we then need to confirm the specific relation sense. Although the sense hierarchy in PDTB has been broadly adopted~\cite{lei2017swim,lei2018linguistic}, we adopt the modified Segmented Discourse Reprsentation Theory (SDRT) hierarchy, defined in STAC dataset \cite{asher2016discourse}, as it is designed specifically for multiparty dialog. There are 16 discourse relations in the STAC schema, as given in Table~6, where the top four most frequent relations (Comment, Clarification Question, Question Answer Pair (QAP), and Continuation) make up over 80\% of the relations in the corpus.

For the discourse parsing task, we used 500 dialogs for development and 500 dialogs for testing which is different from the MRC tasks.  In opposition to the frequent relationships, there are also four types of relations that individually account for less than one percent of the corpus, namely, Alternation, Background, Narration, and Parallel. This is similar to the proportion of these four types of relations in the STAC dataset as well: only 0.5--2.0\%. Next, according to the distribution of all kinds of relations, we need to consider merging some rare relation types in future work, so as to propose a more practical sense hierarchy for multiparty dialogs.

Multi-relational link prediction aims to predict missing links in an edge-labeled graph.  This task focuses on the relations between entities \cite{bordes2013translating}. However, discourse parsing focus on finding the discourse dependency arcs between different utterances.

The discourse dependency structures of Dialog~1 and Dialog~2 are shown in Fig.~3 where each utterance is represented as a node in the dependency graph. The label on the link in the discourse dependency relation.  Dialog~1 ({\it cf.} Table~1) is a multiparty dialog with four speakers: \textit{nbx909},\textit{likwidoxigen}, \textit{babo}, \textit{nuked} and seven utterances. Dialog~2 (\textit{cf.} Fig.~2) has two speakers: \textit{toma-} and \textit{woodgrain}, and eight utterances in total. From Fig.~1, we can find that most of the discourse relations of two-party dialog occurs between adjacent utterances.

\begin{figure}
	\centering
	\includegraphics[width=\textwidth]{dialogue2.pdf}

	\caption{Dialog~2 is a two-party dialog example with eight utterances --- $U_1$ to $U_8$ --- proposed by two speakers: \textit{toma-} and \textit{woodgrain}.}
	\label{fig:framework}
\end{figure}

\begin{figure}
	\centering
	\includegraphics[width=\textwidth]{twoVSmulti.pdf}

	\caption{The discourse dependency structure and relations for Dialog~2 (Left, two-party) and Dialogue~1 (Right, multiparty). Clari\_q, QAP, and Q-Elab are respectively short for Clarification\_question, Question-answer\_pair, and Question-Elaboration. The label on the link represents the discourse dependency relations between two utterances.}
	\label{fig:framework}
\end{figure}

\subsection{Data Quality}

To ensure the quality of the corpus, we adopt two ways to check the annotation: a manual, human check as well as a programmatic check.
\begin{itemize}
	\item {\bf Manually Check}. Two authors of our Molweni dataset sample some instances to check the quality of proposed questions and feedback bad questions to the annotator.
	\item {\bf Programmatic Check}. If answers cannot be found in the source dialog, the annotator would be asked to annotate the dialogs again until passing the check. We additionally check that the questions are grammatically correct using the \textit{Grammarly} web application \footnote{\url{https://app.grammarly.com/}}.
\end{itemize}
After four rounds of revision, we obtain the currently published version of the dataset.

We calculate the Fleiss Kappa value to check on the interannotator consistency.  A Kappa value of 1.0 signifies complete agreement, and a value of 0.0 signifies completely uncorrelated judgments.  Kappa values above  The Kappa value of discourse dependency links is 0.91 which is an almost perfect agreement because the Ubuntu dataset initially contains the response-to relations, and annotators adopt most of the links. The final Kappa value of both links and relations is 0.56 among annotators, close to that of 0.58 obtained in the original STAC corpus.
One reason for the drop of Kappa after labeling relation types is the discourse relation recognition is a multi-label task. There could be more one relation between two utterances in a dialog, which would easily make ambiguities.

\section{Related work}

\paragraph{Discourse parsing for multiparty dialog.} Prior to Molweni, STAC was the only corpus containing annotations for discourse parsing on multiparty chat dialogs \cite{asher2016discourse}. The corpus derives from the online version of the game \textit{The Settlers of Catan}. The game is a multiparty, win--lose game. We introduce the senses of discourse relation in STAC in Section~3.2. The STAC corpus contains 1,091 dialogs with 10,677 utterances and 11,348 discourse relations. Compared with STAC, our Molweni dataset contains 10,000 dialogs comprising 88,303 utterances and 78,245 discourse relations.

\paragraph{Machine reading comprehension.}
There are several types of datasets for machine comprehension, including multiple-choice datasets \cite{richardson2013mctest,lai2017race}, answer sentence selection datasets \cite{wang2007jeopardy,yang2015wikiqa} and extractive datasets \cite{rajpurkar2016squad,joshi2017triviaqa,trischler2017newsqa,rajpurkar2018know} .
To extend existing corpora, our Molweni dataset is constructed to be an extractive MRC dataset for multiparty dialog, which includes both answerable questions and unanswerable questions. Similar to Squad~2.0 \cite{rajpurkar2018know}, we also annotate plausible answers for unanswerable questions. Three closely related datasets are
the extended crowdsourced {\it Friends} corpus \cite{ma2018challenging}, DREAM \cite{sun2019dream} and FriendsQA \cite{yang-choi-2019-friendsqa}. Different from these three MRC datasets for dialog, Molweni contributes the discourse structure of dialogs, and additional instances of multiparty dialogs and unanswerable questions.

\section{The Molweni corpus}

Our dataset derives from the large scale multiparty dialogs dataset --- the Ubuntu Chat Corpus \cite{lowe2015ubuntu}.
We list our three reasons in choosing the Ubuntu Chat Corpus as the base corpus for annotation.
\begin{itemize}
	\item First, the Ubuntu dataset is a large multiparty dataset.

	After filtering the dataset by only retaining all utterances with response relations, there are still over 380K sessions and 1.75M utterances. In each session, there are 3-10 utterances and 2-7 interlocutors.
	\item Second, it is easy to annotate the Ubuntu dataset. The Ubuntu dataset already contains Response-to relations that are discourse relations between different speakers' utterances. For annotating discourse dependencies in dialog, we only need to annotate relations between the same speaker's utterances and the specific sense of discourse relation. Because the length of dialogs in the Ubuntu dataset is not too long, we can easily summarize dialogs and propose some questions for the dialog.
	\item Third, there are many papers doing experiments on the Ubuntu dataset, and the dataset has been widely recognized. For example, \newcite{kummerfeld2019large} proposed a large-scale, offshoot dataset for conversation disentanglement based on the Ubuntu IRC log.

	Also recently, \newcite{GSNijcai2019} also used the Ubuntu Chat Corpus as their dataset for learning dialog graph representation.
\end{itemize}

The discourse dependency structure of each multiparty dialog can be regarded as a discourse dependency graph where each node is an utterance. To learn better graph representation of multiparty dialogs, we filter the Ubuntu Chat Corpus for complex dialogs -- those dialogs with 8--15 utterances and 2--9 speakers.
As multiparty dialog is already intensely complex for the current state of the art, we chose to further simplify in our selection criteria, additionally filtering out dialogs with long utterances (more than 20 words).
Finally, we
randomly chose a subset of $\sim$10,000 dialogs with 88,303 utterances from the Ubuntu dataset.
We give an overview of Molweni's key demographics in Table~1.
\input{overview.tex}

10,000 dialogs are divided into two parts: 100 dialogs in common (public dialog) and 9,900 dialogs for different annotators (private dialog). Each annotator is asked to annotate 1,090 dialogs (990 private dialogs and 100 public dialogs)  in two aspects: machine reading comprehension and discourse structure. All annotators chose to annotate the discourse structure of the dialog, and then propose questions and find answer spans for the dialog.
All annotators agreed that it would be helpful to annotate the MRC task after annotating the discourse structure.

In total, our subjects annotated 9,754 dialogs,
slightly fewer than 10,000 dialogs,
consisting of 88,303 utterances, and contributed 30,066 questions for machine reading comprehension and 78,245 discourse relation annotations.
There are 8,771 dialogs in the demarcated {\sc Train} set for both machine reading comprehension and discourse parsing tasks. 883 dialogs are used for {\sc Dev} set. Each annotator is asked to propose three questions per dialog. There are 100 dialogs in common for all ten annotators, and these 100 dialogs comprise our {\sc Test} set. Each dialog in the training set and develop set has three questions.
Our annotation team proposed a total of 2,871 questions for the 100 dialogs in {\sc Test} sets.

Detailed statistics are shown in Table~2. The average number of speakers per dialog is 3.51, which means that most dialogs are multiparty (as opposed to 2-party) dialogs.
The number of two-party dialogs and multiparty dialogs in our dataset is 2,117 and 7,883, respectively.
In Molweni, the average and maximum length of the selected dialogs are 8.82 and 14 utterances, respectively, and the number of answerable and unanswerable questions are 25,779 and 4,287, respectively.

\input{details}

\input{compare_datasets.tex}

\subsection{Annotation for machine reading comprehension}

We hired ten annotators to construct our Molweni dataset. As the Ubuntu corpus is technical in nature, all annotators are undergraduate students whose major is computer science to annotate the corpus. Annotators are non-native English speakers but who have an English proficiency certificate. They are all familiar with Linux operation system.

Annotators propose three questions for each dialog and annotate the span of answers in the input dialog.
There are two types of questions in our corpus, namely, answerable questions and unanswerable questions:
\begin{enumerate}
	\item \textbf{Answerable questions.} For these questions, the answer is a continuous span from source dialog. Annotators were asked to label answers from input dialog and ensure answers were succinct, without including extraneous text.
	\item \textbf{Unanswerable questions.} To make the reading comprehension task more challenging, we annotate unanswerable questions and their plausible answers (PA). The plausible answers are quite related to unanswerable questions.
\end{enumerate}

We compare Molweni against other datasets in Table~3. We see that existing dialog MRC datasets neither contribute either unanswerable questions, nor annotated discourse structure. Due to the complex structure of multiparty dialogs, we believe that it is essential to adopt the discourse dependency structure for the machine modeling towards multiparty dialog understanding. To the best of our knowledge to date, Molweni is the only MRC dataset that is annotated with discourse structure.

We give example questions from Molweni in Table~4. In particular, most of the questions in our dataset are questions lead by {\it Why}, {\it What}, {\it Who}, {\it Where}, {\it When}, and {\it How}. Only a small proportion of the questions are Other questions; questions lead by words such as {\it Do}, {\it Which}, and {\it Whose}. When annotators propose questions, they are asked to consider the characteristics of multiparty dialogs. For example, for {\it Why} and {\it How} questions, it is essential to know the question--answer pair and the cause--result in the dialog. For {\it How} questions, it is important to understand the role of speakers in order to properly represent the multiparty dialog. As such, {\it Why} and {\it How} often require a deeper understanding of the dialog.

\input{5W1H_questions.tex}

\subsection{Annotation for discourse structure of multiparty dialogs}

The task of discourse parsing for multiparty dialogs is to determine the discourse relations among utterances.  To enable better future modeling of  such multiparty discourse, we represent a multiparty dialog by a directed acyclic graph (DAG). The process of annotating the discourse structure consists of two parts: predicting the links between utterances, and classifying the sense of the resultant discourse relation. Table~5 gives an overview of the statistics for Molweni's discourse parsing annotations.

\input{dp_overview}

An edge between two utterances represents the existence of a discourse dependency relation. The direction of the edge represents the direction of discourse dependency. In this subtask, what annotators need to do is to confirm whether two utterances have a discourse relation. Following the convention in the Penn Discourse Treebank (PDTB) \cite{prasad2008penn}, we term the two utterances as \textit{Arg1} and \textit{Arg2},
sequentially.

\input{relations}

Discourse relations on two non-adjacent utterances are rare in prose but common in multiparty dialogs. When we annotate dialogs, annotators should sequentially read the dialog from its beginning  to its final utterance. For each utterance, annotators need to find at least one parent node from among the previous utterances. We assume that the discourse structure is a connected graph and no utterance is isolated.

After we find the discourse relation between two utterances, we then need to confirm the specific relation sense. Although the sense hierarchy in PDTB has been broadly adopted~\cite{lei2017swim,lei2018linguistic}, we adopt the modified Segmented Discourse Reprsentation Theory (SDRT) hierarchy, defined in STAC dataset \cite{asher2016discourse}, as it is designed specifically for multiparty dialog. There are 16 discourse relations in the STAC schema, as given in Table~6, where the top four most frequent relations (Comment, Clarification Question, Question Answer Pair (QAP), and Continuation) make up over 80\% of the relations in the corpus.

For the discourse parsing task, we used 500 dialogs for development and 500 dialogs for testing which is different from the MRC tasks.  In opposition to the frequent relationships, there are also four types of relations that individually account for less than one percent of the corpus, namely, Alternation, Background, Narration, and Parallel. This is similar to the proportion of these four types of relations in the STAC dataset as well: only 0.5--2.0\%. Next, according to the distribution of all kinds of relations, we need to consider merging some rare relation types in future work, so as to propose a more practical sense hierarchy for multiparty dialogs.

Multi-relational link prediction aims to predict missing links in an edge-labeled graph.  This task focuses on the relations between entities \cite{bordes2013translating}. However, discourse parsing focus on finding the discourse dependency arcs between different utterances.

The discourse dependency structures of Dialog~1 and Dialog~2 are shown in Fig.~3 where each utterance is represented as a node in the dependency graph. The label on the link in the discourse dependency relation.  Dialog~1 ({\it cf.} Table~1) is a multiparty dialog with four speakers: \textit{nbx909},\textit{likwidoxigen}, \textit{babo}, \textit{nuked} and seven utterances. Dialog~2 (\textit{cf.} Fig.~2) has two speakers: \textit{toma-} and \textit{woodgrain}, and eight utterances in total. From Fig.~1, we can find that most of the discourse relations of two-party dialog occurs between adjacent utterances.

\begin{figure}
	\centering
	\includegraphics[width=\textwidth]{dialogue2.pdf}

	\caption{Dialog~2 is a two-party dialog example with eight utterances --- $U_1$ to $U_8$ --- proposed by two speakers: \textit{toma-} and \textit{woodgrain}.}
	\label{fig:framework}
\end{figure}

\begin{figure}
	\centering
	\includegraphics[width=\textwidth]{twoVSmulti.pdf}

	\caption{The discourse dependency structure and relations for Dialog~2 (Left, two-party) and Dialogue~1 (Right, multiparty). Clari\_q, QAP, and Q-Elab are respectively short for Clarification\_question, Question-answer\_pair, and Question-Elaboration. The label on the link represents the discourse dependency relations between two utterances.}
	\label{fig:framework}
\end{figure}

\subsection{Data Quality}

To ensure the quality of the corpus, we adopt two ways to check the annotation: a manual, human check as well as a programmatic check.
\begin{itemize}
	\item {\bf Manually Check}. Two authors of our Molweni dataset sample some instances to check the quality of proposed questions and feedback bad questions to the annotator.
	\item {\bf Programmatic Check}. If answers cannot be found in the source dialog, the annotator would be asked to annotate the dialogs again until passing the check. We additionally check that the questions are grammatically correct using the \textit{Grammarly} web application \footnote{\url{https://app.grammarly.com/}}.
\end{itemize}
After four rounds of revision, we obtain the currently published version of the dataset.

We calculate the Fleiss Kappa value to check on the interannotator consistency.  A Kappa value of 1.0 signifies complete agreement, and a value of 0.0 signifies completely uncorrelated judgments.  Kappa values above  The Kappa value of discourse dependency links is 0.91 which is an almost perfect agreement because the Ubuntu dataset initially contains the response-to relations, and annotators adopt most of the links. The final Kappa value of both links and relations is 0.56 among annotators, close to that of 0.58 obtained in the original STAC corpus.
One reason for the drop of Kappa after labeling relation types is the discourse relation recognition is a multi-label task. There could be more one relation between two utterances in a dialog, which would easily make ambiguities.

\section{Related work}

\paragraph{Discourse parsing for multiparty dialog.} Prior to Molweni, STAC was the only corpus containing annotations for discourse parsing on multiparty chat dialogs \cite{asher2016discourse}. The corpus derives from the online version of the game \textit{The Settlers of Catan}. The game is a multiparty, win--lose game. We introduce the senses of discourse relation in STAC in Section~3.2. The STAC corpus contains 1,091 dialogs with 10,677 utterances and 11,348 discourse relations. Compared with STAC, our Molweni dataset contains 10,000 dialogs comprising 88,303 utterances and 78,245 discourse relations.

\paragraph{Machine reading comprehension.}
There are several types of datasets for machine comprehension, including multiple-choice datasets \cite{richardson2013mctest,lai2017race}, answer sentence selection datasets \cite{wang2007jeopardy,yang2015wikiqa} and extractive datasets \cite{rajpurkar2016squad,joshi2017triviaqa,trischler2017newsqa,rajpurkar2018know} .
To extend existing corpora, our Molweni dataset is constructed to be an extractive MRC dataset for multiparty dialog, which includes both answerable questions and unanswerable questions. Similar to Squad~2.0 \cite{rajpurkar2018know}, we also annotate plausible answers for unanswerable questions. Three closely related datasets are
the extended crowdsourced {\it Friends} corpus \cite{ma2018challenging}, DREAM \cite{sun2019dream} and FriendsQA \cite{yang-choi-2019-friendsqa}. Different from these three MRC datasets for dialog, Molweni contributes the discourse structure of dialogs, and additional instances of multiparty dialogs and unanswerable questions.

redpajama_set_name: RedPajamaArXiv


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For a full copy (including table) of this meeting notice, please copy and paste: https://www.deq.virginia.gov/Portals/0/DEQ/Water/TMDL/PublicNotices/2019/11523_NFRivanna_TM_public_notice_TAC3_20190227.pdf This Technical Advisory Committee meeting will be open to the public and all are welcome. In the case of inclement weather, the meeting will be held at the same time and location on March 6, 2019. For more information, please contact staff person listed below. MEETING DESCRIPTION: A Technical Advisory Committee will hold a meeting to discuss the development of a Total Maximum Daily Load (TMDL) for two segments of the North Fork Rivanna River and several of its tributaries. Discussions will include a review of sediment and phosphorous loading estimates from all pollutant sources in the watershed as well as allocation scenarios. These scenarios will demonstrate the pollutant reductions needed from different sources to meet water quality improvement goals. Section 303(d) of the Clean Water Act and §62.1-44.19:7.C of the Code of Virginia requires DEQ to develop TMDLs for pollutants responsible for each impaired water contained in Virginia's 303(d) TMDL Priority List and Report. DESCRIPTION OF STUDY: The North Fork Rivanna River and several of its tributaries in Greene and Albemarle Counties have been placed on Virginia's impaired waters list for failing to support the benthic water quality standard. This standard is intended to protect the aquatic life designated use, which states that all of the Commonwealth's waterways will support a diverse and abundant population of aquatic life. This water quality study will include a benthic stressor analysis to determine the most likely pollutant(s) responsible for the impairments, and it will report on the sources of these pollutant(s) and recommend reductions to meet a Total Maximum Daily Load (TMDL) for the impaired stream segments. A TMDL is the total amount of a pollutant a water body can contain and still meet water quality standards. To restore water quality in the North Fork Rivanna River and its tributaries, pollutant levels will need to be reduced to the TMDL amount. Through this process, Virginia agencies will partner with a Technical Advisory Committee made up of local stakeholders to identify sources of pollution impacting aquatic life in these waterways, and the reductions needed from these sources to meet the TMDL. For more information, please contact the staff person listed below.


text:
Jim Herndon, MD, MBA
Hand & Arm
Chairman Emeritus, Mass General Brigham Department of Orthopaedic Surgery
Hand and Upper Extremity Orthopaedic Surgeon
William H. and Johanna A. Harris Professor of Orthopaedic Surgery, Harvard Medical School
Boston, MAPhone: 617-726-2784
About Jim Herndon, MD, MBA
Dr. Jim Herndon is a leading orthopaedic surgeon, educator and researcher who has made important advances in orthopaedic surgery, education, professional leadership, and patient safety.
Dr. Herndon has a special interests in degenerative diseases and trauma reconstruction of the upper extremity. He has published more than 200 papers, five books and 31 book chapters. Dr. Herndon is a frequent lecturer at scientific meetings around the world and led the first clinical trial using gene therapy for rheumatoid arthritis.
Dr. Herndon received his MD from UCLA in 1965 and completed his orthopaedic residency in 1970 at Children's Hospital and Mass General Hospital in Boston. From 1971 to 1973, he was a major in the US Army, serving as Chief of the Amputee Service at Valley Forge General Hospital in Phoenixville, PA. He then completed a fellowship in reconstructive hand surgery in 1974 at Roosevelt Hospital in New York City.
Dr. Herndon has the unique distinction of having served as President of all important Orthopaedic Societies: American Academy of Orthopaedic Surgeons, American Board of Orthopaedic Surgery and American Orthopaedic Association. He also served as chairman of the Residency Review Committee for Orthopaedic Surgery.
Prior to joining MGH in 1998, Dr. Herndon was Professor and Chairman of Orthopaedic Surgery at the University of Pittsburgh Medical Center from 1988 to 1998 and Professor and Chairman of Orthopaedics at Brown University from 1978 to 1988. He has held additional academic appointments at the University of Pennsylvania School of Medicine, Columbia University College of Physicians and Surgeons, and Michigan State University.
Dr. Herndon also holds a MBA in health care management from Boston University. He has received honorary degrees from Brown University, Harvard Medical School and Loyola-Marymount University. Recently, Dr. Herndon was elected an Honorary Fellow of The Royal College of Surgeons of England.
Hand & Upper Extremity Service
Departments, Centers, & Programs:
Hand & Arm Center
Clinical Interests:
Nerve injuries
Wrist problems
Treats:
Adult & Pediatrics
Mass General Orthopaedics
Yawkey Center for Outpatient Care
MBA, Boston University
MD, UCLA School of Medicine
MD, University of California, Los Angeles, School of Medicine
, Valley Forge General Hospital
Residency, Boston Children's Hospital
Residency, Children's Hospital Boston
Residency, Hospital of the University of Pennsylvania
Residency, Massachusetts General Hospital
Residency, University of Pennsylvania School of Medicine
Fellowship, Roosevelt Hospital
American Board Certifications
Hand Surgery, American Board of Orthopaedic Surgery
Hand Surgery, American Board of Orthopedic Surgery
Orthopaedic Surgery, American Board of Orthopedic Surgery
Orthopedic Surgery, American Board of Orthopaedic Surgery
Aetna Health Inc.
AllWays Health (NHP) - ACD
AllWays Health (NHP) - PBO
Blue Cross Blue Shield - Blue Care 65
Blue Cross Blue Shield - Indemnity
Blue Cross Blue Shield - Managed Care
Blue Cross Blue Shield - Partners Plus
BMC HealthNet Mass Health MCO/ACO
Centene/Celticare
Cigna (PAL #'s)
Fallon Community HealthCare
Great-West Healthcare (formally One Health Plan)
Harvard Pilgrim Health Plan - ACD
Harvard Pilgrim Health Plan - PBO
Health Care Value Management (HCVM)
Humana/Choice Care PPO
Maine Community Health Options (MCHO)
Medicare - ACD
OSW - Maine
OSW - New Hampshire
OSW - New York
OSW - Rhode Island
Private Health Care Systems (PHCS)
Railroad Medicare - ACD
United Healthcare (non-HMO) - ACD
United Healthcare (non-HMO) - PBO
Well Sense Pediatrics
Note: This provider may accept more insurance plans than shown; please call the practice to find out if your plan is accepted.
Dr. Herndon has made significant contributions in general orthopaedics, including adult respiratory distress after fractures, fat embolism with total hip replacement, management of war-injured amputees, scoliosis, as well as in hand and upper extremity, including arthritis, joint reconstruction, tendon and nerve injuries, and congenital problems.
Dr. Herndon was a pioneer in gene therapy for rheumatoid arthritis and led the first gene therapy clinical trial in the US. His other research interests include biomechanics of the forearm and the interosseous ligament, tendon injuries, and nerve healing.
Dr. Herndon continues to be interested in quality of care, especially regarding patient safety and the reduction of medical errors.
Select Publications:
Herndon JH, Allan BJ, Dyer G, Jawa A, Zurakowski D. Predictors of success on the American Board of Orthopaedic Surgery Examination, Clin Ortho & Related Research. 467:2436-2445, Sept. 2009.
Wong D, Lewis B, Herndon J, Martin C, Brooks, R. Patient safety in North American: beyond "Operate through your Initials" and "Sign your Site". J Bone Joint Surg Am. 91:1534-41, 2009.
Wong DA, Herndon JH, Canale ST, Brooks RL, Hunt TR, Epps HR, Fountain SS, Albanese SA, Johanson NA. Medical errors in orthopaedics: results of an AAOS member survey. J Bone Joint Surg Am. 91:547-557, 2009.
Huddleston J, Maloney W, Verzier N, Wang Y, Hunt D, Herndon J. Adverse events in total knee arthroplasty. A national Medicare study. Journal of Arthroplasty, 24:Issue 2, Feb 2009.
Lieberman I, Herndon J, Hahn J, Fins JJ, Rezai A. Surgical innovation and ethical dilemmas: a panel discussion. Cleveland Clinic Journal of Medicine, Volume 75, Supplement 6 S13, 2008.
Secure online access to your health information whenever you need it. Check appointments, communicate with your provider and pay bills online 24/7.
Go to Patient Gateway

redpajama_set_name: RedPajamaCommonCrawl


text:
Hulu's Top Picks
March 28, 2021 by SR~Dailey, posted in School News
Salvatore Bonasia '22
According to Hulu, here are the Top Ten Shows/Films out now to watch! :
1. Ramy – TV Series: (TVMA) – Season 2 (2019):
"Ramy, a first generation Egyptian-American, is on a spiritual journey in his politically divided neighborhood. Ramy will bring a fresh viewpoint to the screen as it examines the complexities of being trapped between traditional Muslim culture, that sees life as a moral examination, and a millennial generation that sees life awash with opportunities." (Hulu)
2. Justified – TV Series: (TVMA) – Season 6 (2010):
"A law enforcement officer is drawn back to his home state of Kentucky. The long-brewing feud between Raylan and Boyd Crowder will finally come to a head." (Hulu)
3. Better Things – TV Series: (TVMA) – Season 4 (2016):
"A single mother and actress in Los Angeles is struggling to raise her three daughters, and faces the everyday challenges that come from having such a unique family." (Hulu)
4. Atlanta – TV Series: (TVMA) – Season 2 (2016):
"Young rappers in Atlanta are attempting to begin their careers in the music industry." (Hulu)
5. What We Do in the Shadows – TV Series: (TVMA) – Season 2 (2019):
"A glimpse into the lives of four vampires who've been together for centuries. After a visit from their dark lord and leader, they're told of their reason for landing in New York City over a hundred years ago." (Hulu)
6. Pen15 – TV Series: (TVMA) – Season 2 (2019):
"In the year 2000, thirteen-year-old outcasts are surrounded by real thirteen-year-olds, and the best day of their lives will turn into the worst with a stroke of a gel pen." (Hulu)
7. Harlots – TV Series: (TVMA) – Season 3 (2017):
"Margaret Wells is torn between her position as owner of a brothel and as a mother to her daughters Charlotte and Lucy. Margaret must fight back when her company is threatened by Lydia Quigley, a vicious rival madam, even though it could mean losing her family, or even her life." (Hulu)
8. Mrs. America – TV Series: (TVMA) – Season 1 (2020):
"Mrs. America tells the tale of the fight to ratify the Equal Rights Amendment,and the unlikely backlash led by Phyllis Schlafly, "the silent majority's sweetheart."" (Hulu)
9. Difficult People – TV Series: (TVMA) – Season 3 (2015):
"Their typical, irreverent behavior places them in some very uncomfortable circumstances as best friends living in New York City." (Hulu)
10. You're The Worst – TV Series: (TVMA) – Season 5 (2014):
"Self-destructive, selfish, and brash, all marriages, according to Jimmy Shive-Overly, are doomed. "I'm cynical, people-pleasing, and obstinate." Gretchen Cutler understands that she isn't cut out for relationships. It's only natural that when they meet at a wedding, the two of them go home together, and against their better judgement, begin to fall for each other." (Hulu)
You should definitely go watch them right now!
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text:
The divorce judge asked little Johnny which parent he wanted to live with. Little Johnny replied, "Not my daddy, he beats me…Not my mommy, either; she beats me, too." Little Johnny thought for a minute, then exclaimed, "I know! I want to live with the New Orleans Saints! They don't beat anybody!"
Latest Lawyer Jokes
Oh, those darn lawyers
Two lawyers, Jon and Amanpreet, head out for their usual 9 holes of golf. Jon offers Amanpreet a $50 bet. Amanpreet agrees and they're off. They shoot a great game. After the 8th hole, Amanpreet is ahead by one stroke, but cuts his ball into the rough on the 9th.
"Help me find my ball. Look over there," he says to Jon. After a few minutes, neither has any luck. Since a lost ball carries a four point penalty, Amanpreet secretly pulls a ball from his pocket and tosses it to the ground. "I've found my ball!" he announces.
"After all of the years we've been partners and playing together," Jon says, "you'd cheat me out of a lousy 50 bucks?"
"What do you mean, cheat? I found my ball sitting right there!"
"And you're a liar, too!" Jon says. "I'll have you know I've been STANDING on your ball for the last five minutes!"
The Heart Of The Matter
A man who was having heart trouble went to the doctor to see what his options were. Naturally, the doctor recommended a heart transplant. The man reluctantly agreed, and asked if there were any hearts immediately available, considering that money was no object.
"I do have three hearts," said the doctor. "The first is from an 18-year old kid, non-smoker, athletic, swimmer, with a great diet. He hit his head on the swimming pool and died. It's $100,000. The second is from a marathon runner, 25 years old, great condition, very strong. He got hit by a bus. It's $150,000. The third is from a heavy drinker, cigar smoker, steak lover. It's $500,000."
"Hey, why is that heart so expensive? He lived a terrible life!"
"Yes, but it's from a laywer. It's never been used."
Farmer Joe and his Mule
Farmer Joe decided his injuries from his recent accident were serious enough to take the trucking company responsible for the accident to court. In court, the trucking company's fancy lawyer was questioning farmer Joe.
"Didn't you say, at the scene of the accident, that you were fine?"
"Well, I'll tell you what happened. I had just loaded my favorite mule Bessie into the–"
"I didn't ask for any details," the lawyer interrupted. "Just answer the question. Did you not say, at the scene of the accident, that you were fine?"
"Well I had just got Bessie into the trailer and was driving down the road–"
"Judge, I am trying to establish the fact that, at the scene of the accident, this man told the Highway Patrolman on the scene that he was just fine. Now several weeks after the accident he is trying to sue my client. I believe he is a fraud. Please tell him to simply answer the question." By this time the Judge was fairly interested in Farmer Joe's answer and told the lawyer so.
"Well," said the farmer, "as I was saying, I had just loaded Bessie, my favorite mule, into the trailer and was driving her down the highway when this huge semi-truck and trailer ran the stop sign and smacked my truck right in the side. I was thrown into one ditch and Bessie was thrown into the other. I was hurting real bad and didn't want to move. However, I could hear ol' Bessie moaning and groaning. I knew she was in terrible shape just by her groans. Shortly after the accident a Highway Patrolman came on the scene. He could hear Bessie moaning and groaning so he went over to her. After he looked at her he took out his gun and shot her between the eyes. Then the Patrolman came across the road with his gun in his hand and looked at me. He said, 'Your mule was in such bad shape I had to shoot her. How are you feeling?"
Lawyer Send Me
NASA was interviewing professionals to be sent to Mars. Only one could go, but with one catch – he couldn't return to Earth.
The first applicant, an engineer, was asked how much he wanted to be paid for going. "One million dollars," he answered, "because I want to donate it to M.I.T."
The next applicant, a doctor, was asked the same question. He asked for two million. "I want to give a million to my family," he explained, "and leave the other million for the advancement of medical research."
The last applicant was a lawyer. When asked how much money he wanted, he whispered in the interviewer's ear, "Three million dollars."
"Why so much more than the others?" the interviewer asked.
The lawyer replied, "If you give me $3 million, I'll give you $1 million, I'll keep $1 million, and we'll send the engineer."
Three questions
A man walked into a lawyer's office and inquired about the lawyer's rates.
"$50.00 for three questions", replied the lawyer.
"Isn't that awfully steep?" asked the man.
"Yes," the lawyer replied, "and what was your third question?"


Major disability insurance companies know that for every ten denial letters they issue, only a couple of claimants will make their way to an experienced long term disability company. This factor is a good thing for the insurance companies.
Recently an executive came to us after his long term disability claim had been terminated. He had a job that required a lot of complex thinking. The job was an "office job," so, from a physical standpoint, it was labeled sedentary.
He suffered a stroke, and the stroke had caused significant cognitive deficits and chronic, severe pain.
Unbeknownst to the claimant, Lincoln Financial had his records evaluated by a mental health professional who told Lincoln Financial that the man's cognitive deficits were significant and prevented him from working in his executive position.
Lincoln Financial then had the case reviewed again, this time by an internist. They did NOT send the neuropsychological records to the internist. The internist determined that even though the man had had a stroke, he could still "lift 10 pounds" and sit, stand, and walk through out the day, so he was able to do his sedentary job.
As soon as it received the internist's report that the man could, from a physical standpoint, do a sedentary job, they terminated his benefits, sending he and his family into a financial tailspin.
They never told the claimant of the favorable report they had procured from their own expert.
This is a dishonest practice. Shame on Lincoln.
Here's the teaching point: when you get a denial letter from a disability insurance company you should have it reviewed by an experienced long term disability attorney. Some of these insurance companies engage in the most dishonest practices imaginable.


Take to the sea! We rent sail boats to qualified sailors. Click to see prices.
Sail Monterey Bay, Book Now!
Sail beautiful Monterey Bay! We offer daily public and private cruises as well as our famous sunset appetizer cruise & much more!
Give the gift of sailing! Click here to buy a gift card for a friend or a loved one and have it immediately emailed to you.
Sail Monterey, the only sailing company that departs from the Old Fisherman's Wharf in Monterey. Our fleet is comprised of five different sailing vessels. So, whatever your sailing taste, big boat or small, mono hull or catamaran, Sail Monterey can cater an experience to your dream excursion.
Sailing is an eco-friendly way to see the bay. Our sail boats use the power of the wind on 90% of each trip, burning diesel only to exit and enter the harbor. Meanwhile, other boating trips consume huge amounts of fossil fuels.
Our guests also love that our cruises are limited to six passengers per trip. Smaller capacity cruises provide a much more quiet and intimate experience, and a chance to sit next to the captain and ask questions. We employ many experienced captains, all who love sharing their passion for sailing and the beauty of the bay. So don't be afraid to learn how sailing works or ask why sea lions never stop barking!
Have you always wanted to learn to sail? Our licensed instructors have taught and certified boat loads of happy students. We proudly offer American Sailing Association certified courses. ASA certifications are valid for life and recognized internationally. We are dedicated to teaching safety and seamanship and give you the confidence on the water. Once certified, you can qualify to rent our boats.
If you would like a casual pace, we also offer individual sailing lessons for those that want more time on the water to hone their skills.
Can I Rent a Sail Boat?
Here at Sail Monterey, we specialize in sailing cruises on the bay. Whether you want to sit back and sip wine or have a hands on sailing experience, we cater the cruise to your liking. Let us know what you are looking for and we will make it happen.
Capt. Paul provided an outstanding tour of the bay! Experience was as if a friend took us out on his boat. Excellent directions to the landing point and perfect recommendations for local lunch restaurants. Definitely recommend this tour!
My daughter and I rented the 22ft Catalina and sailed off for three hours into the bay. These guys were really professional. They gave me a radio, a cellphone number and they had anticipated little extra details on board such as a jackets, drinking water and sunscreen.
We had a wonderful time on the cruise with Capt Dan. We were with two other couples and the captain who manned the sail boat all by himself! The snacks were yummie, the wine was plentiful and the views! Wow! We were able to see tons of otters with their kittens, sea lions and we even saw a few whales in the distance! Not to mention the beautiful sunset over the water. 10/10!
Really enjoyed our captain, Wim, who was personable and friendly. Perfect impromptu sailing excursions which are intimate for a casual price. Sailing boats available were in really good shape and fun to be in.
My husband and I are learning how to sail and Paul has been a very kind, fun and professional teacher. He is passionate about sailing and it is obvious that this is not just a job for him. We are so excited to continue learning under his tutelage. Futhermore, the boat is beautiful, comfortable and impeccably maintained. We always feel safe and comfortable.


Christmas Archives - Page 2 of 3 - TheUrbanMusicScene.com | Music Reviews!
celebrated commercial time of the year.
The Holiday Season. Beckoned with warmth, seasonal temperature changes & gift wrapping paper scattered all over the tables & floors. Lets not forget about the many spices & candles, the outdoor home light fixtures & a traditional choice of the decorated Christmas Tree. Along with visiting family not seen in recent years & so forth. That's if you celebrate Christmas that way. Many do. Many don't.
Wendy Moten's beautifully timed and gorgeously tuned voice has graced the music industry for almost two decades, even if her solo recording exposure was somewhat unappreciated. No matter what the genre, her instrument was well utilized with international superstars, both in the studio and in concert. From Michael Bolton, to Faith Hill & Tim McGraw and Julio Iglesias, Moten was a much in-demand backing and featured vocalist.
If there is anybody who could jump start the holiday season with high class, Take 6 would win hands down. This sextet continues to bring their brand of a cappella stylized gospel to the forefront since they bowed on the national scene in 1987. Their humble beginnings were etched on the campus of Oakwood College in Huntsville, Alabama in 1980 as a quartet under the moniker Alliance.
There was a surefire electric night at the Gospel Music Channel's studio in Atlanta, Georgia because Ann Nesby was in the building. What a wonderful gesture to present a special Christmas gift – Ann Nesby's Soulful Christmas – to all her fans after releasing a disc earlier this year – The Lula Lee Project.
When Jay Beckenstein was asked how the name Spyro Gyra was birthed, he took the inspiration from a biology class called the spirogyra; also known as pond scum. Some persons might chuckle at such a choice. But no one could possibly laugh at their major musical impact especially for the smooth jazz movement, spanning a twenty-six disc resume.
Whenever Patti Austin's name comes to my mind, a reflection of deeply rooted rhythm & blues music illuminates. A sense, or shall I say, a great memory of classic joys of 'Havana Candy' automatically drifts into play.
What a unique way to not only feature a great selection of special music artists to an evening of fantastic, special Christmas music on stage. But to demonstrate how well WHUR/Howard University Records had produced this concert with proceeds to benefit their "Food2Feed" nonprofit organization, designed to help Washington DC area families with monetary donations & non-perishable food items.
Bring in the Holiday Season with Brian McKnight and his New Christmas Album I'll Be Home For Christmas. You'll enjoy your all time favorites along with new ballads he crooned to. Brian McKnight is excellent. He stayed true to his craft and produced a great Christmas album.
I must confess as an avid music connoisseur that when a popular vocalist records a Christmas project, especially a multi-million selling artist, curiosity almost sets in immediately. The main test question: Will they make a strong and lasting impression with their interpretations of holiday standards, favorite Carols, and original material? In the case of Coko's Christmas musical present – A Coko Christmas – the former lead vocalist of SWV (Sisters With Voices) does a fairly credible job with the familiar tunes, two new urban holiday tracks, and some blasts from the past.


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text:
Yellow Paper
Constitution of Hindustan Republican Association
(1924) by Ram Prasad Bismil
(A core study document for India Against Corruption members)
This historical document is also known as HRA's Yellow Paper. Copies of this leaflet were also found with some other alleged conspirators of Kakori Conspiracy Case as per Judgement of the Chief Court Of Oudh published at Newul Kishore Press Lucknow in 1927. This document can be seen in the National Archives of India New Delhi in the Home Department file K.W. file no. 53/27 (Political). This document was delivered on 22 August 1927 by the British Government in the Criminal Appeals of Ram Prasad Bismil and others versus the King-Emperor.
The name of the Association shall be the Hindustan Republican Association.
The object of the association shall be to establish a Federated Republic of the United States of India by an organized and armed Revolution.
The final form of the constitution of the Republic shall be framed and declared by the representatives of the people at the time when they will be in a position to enforce their decisions.
The basic principle of the Republic shall be Universal Suffrage and the abolition of all systems which make any kind of exploitation of man by man possible.
The Governing Body of the Association shall be the Central Council composed of the representatives of every province of India. All decisions of the Central Council shall be vested with absolute powers.
The principle function of the Central Council shall be to supervise, adjust and coordinate the activities in the different provinces of which it shall have full knowledge.
The Central Council shall be in direct charge of the work to be carried on in countries outside India.
Provincial Organisation
There shall be a committee, ordinarily of five men representing the five (5) different departments of the Association in every province, which shall regulate all the activities of Association in the province.
All decisions of the committee shall be arrived at by unanimous consent. Departments
Every provincial organisation shall have the following departments:
Propaganda,
Collection of men,
Collection of funds and terrorism,
Collection and storage of arms and ammunitions,
Foreign connection.
1. Propaganda shall be carried on:
(a) by an open and a secret press,
(b) through private conversation,
(c) through public platforms,
(d) through a system of organized Kathas, and
(e) though magic lantern slides.
2. Collection of men shall be done by organisers in charge of different districts.
3. Funds shall be collected generally by means of voluntary subscriptions and occasionally by contributions exacted by force. In extreme cases of repressions by agent or agents of a foreign government, it shall be the duty of the association to retaliate in whatever form it shall consider suitable.
4. Every effort shall be made to arm every member of the association; but all such arms shall be stored at different centres and be used according to the directions of the provincial committee only.
No arms shall be removed from any place in the district or used without knowledge and the permission of the district organiser or the officer-in-charger of this department.
5. Foreign Connection. This department shall carry on its work under the direct orders from the CC.
District Organiser - His Duties
The district organiser shall be in sole charge of the members of his district.
He shall try to start branches of this association in every part of his district. In order to have efficient recruitment he should keep himself in touch with the different public bodies and institutions in his district.
The district organisers shall be subordinate in every way to the provincial committee who shall supervise and direct all the activities.
The district organiser must see that the member are divided into separate groups and the different groups do not know each other.
So far as possible the district organisers of any province must not know the activities of each other and, if possible, they must also not know each other by person or by name. No district organiser shall leave his station without previously informing his superior.
Qualifications of a District Organiser
He must have the tact and the ability to guide and handle men of different temperaments.
He must have the capacity to grasp political, social and economical problems of the present day with special reference to his motherland.
He must be able to grasp the spirit of the history of India, with special reference to the particular civilisation which India has evolved.
He must have faith in the mission and the destiny of a free India, which is to bring harmony in the different spheres of human activities, both spiritual and material.
He must be courageous and self-sacrificing without which all his brilliant qualities will have no real value.
Provincial Council and Central Council
PC and CC members must see that every member of this association gets full scope and ample opportunity to develop and use his individual abilities, without which the association will tend to disintegrate. Programme
All the activities of the association shall be divided into two parts: public and private.
(a) To start association in the forms of clubs, libraries, seva samitis and the like.
(b) To start labour and peasant organisations. Suitable men must be engaged on behalf of the association to organize an control the labourers in the different factories, the railways and in the coalfields, and instil into their minds that they are not for the revolution but that the revolution is for them. Similarly the Kisans must also be organized
(c) To start weekly paper in every province to propagate the idea of an independent Indian Republic.
(d) To publish booklets and pamphlets with a view to enlighten public as to the course of events and the current of thoughts as prevalent in the countries outside India.
(e) To utilise and influence the Congress and other public activities as far as possible.
(a) To establish a secret press and through it to publish such literature which cannot easily be published openly.
(b) To circulate such literature.
(c) To establish branches of this association in every part of the country, district by district.
(d) To collect funds in as many ways as possible.
(e) To send suitable men to foreign countries where they may get military or scientific training so that they may become military or scientific experts to take charge of armies and ammunition factories at the time of open rebellion.
(f) To import arms and ammunitions and also to manufacture them, as far as possible, in the country.
(g) To remain in close touch and to cooperate with the Indian revolutionaries outside India.
(h) To get the members of the association enlisted into the present army.
(i) To enlist the sympathy of the public to our cause by occasional retaliatory measures and propaganda and thus create a band of sympathisers.
All members shall be recruited by organisers in charge of different districts in every province. Every member must be ready to devote his whole time for the association and to risk his life if necessary.
He must obey the commands of the district organiser implicitly.
He should develop his own initiative and remember that the success of the association depends much on the resourcefulness, the initiative and the sense of duty of its individual members.
He must behave in a manner that may not prejudice the cause for which his association stands or may not do any harm directly or indirectly to his organisation.
No member of this association shall belong to any other organisation without the consent of the district organiser.
No member shall leave his station without informing the district organiser about it. Every member must try to avoid being suspected of evolutionary connections by the police or public.
Every member must remember that his individual behaviour and mistakes might lead to the ruin of the whole organisation.
No member shall conceal anything from the district organiser as far as his public life is concerned.
Members who will betray shall be punished either with expulsion or death. The authority of punishment shall rest entirely with the "PC".


(AP) Forecasters are upgrading the likelihood that severe storms and strong tornadoes could strike parts of the South less than a week after a twister killed 23 people in Alabama.
A region that includes parts of Arkansas, Mississippi, Louisiana, Tennessee, Kentucky, Illinois, and Missouri will be at heightened risk of severe weather Saturday, the national Storm Prediction Center reported Friday. The area is home to 2.6 million people and includes the Memphis, Tennessee metropolitan area.
A broader area that includes more than 10 million people — stretching from east Texas to Alabama, Tennessee, and Kentucky — is at a slightly lower risk of severe storms and tornadoes.
"It means you have to take action when warnings are issued and not wait until you see the threat visually," he said. "If you wait until you see an approaching tornado with damaging winds, it's going to be at your location within seconds."
"It can be more difficult to reach people at night," Bunting said. "Just make sure you know how to the get the warnings if it's in the middle of the night."
Forecasters across the region were studying computer models and tracking the system's progress. Some characteristics, such as strong wind shear, have them concerned.
"It's sort of like making a cake — if the ingredients can all really come together we could certainly have multiple tornadoes across the broad region," said Andrew Pritchett, a meteorologist at the National Weather Service in Huntsville, Alabama.
"So it's a very delicate balance — we don't need the rain, but we're hoping that maybe the atmosphere will stay more rain-cooled throughout the day and help mitigate the severe weather threat," he said.


Israel is one of the most moral countries on the face of the earth. Many people have come to Israel because they were not free to be who them are in other parts of the world. Those who want to help this nation today have many ways to do so. They can offer financial donations to Israel directly. Offering donations to Israel can help the people living here in many important ways.
Donations to Israel can provide a means of supporting the population directly as well as offering nutritional help for poor families who may otherwise not have a means of paying the bills for their families and making sure they can have enough to eat each day. Such donations also help do important things like providing for those who have survived the holocaust and may have no other place to turn other than such donations to find what they need. This is one way in which people who want to engage in important charitable giving can give and know that all of the money they choose to donate will be used in the best possible way. Each dollar sent here is one that is used to directly help the population.
Such help also helps people with other tasks such as celebrating the Jewish holidays. Celebrating the Jewish holidays can be hard for those who do not have the means to create the meals they would like to serve to their families. When people are donating to this country, they are helping people who really need such help and offering them the chance to have a better life. In the process, many people are happy to learn exactly where their tax dollars can go and how they can be used to give people really honest help.


Steve was the most helpful lender I've ever dealt with! Fastest closing and best help. Always available! I liked how online signing sped up processes. From start to finish a great experience. Thanks for all your help!
Steve and crew were always there for questions and always made time to help. Painless!
I had a super team behind me all the way! Steve was incredibly helpful throughout my entire experience, and I couldn't have asked for a better person to work with. I greatly appreciate everything you (Steve), and your team have done to help my dream happen so quickly and painlessly.
For over 30 years I have been assisting the home buyers and homeowners of South Central Pa and Northern Maryland. Whether you are buying or refinancing, my extensive experience and contacts allow me to guide you through the mortgage process in an efficient, timely manner.
My honesty and hard work have earned me the reputation as the "Go To Guy" when you" want it done right". I'm especially knowledgeable in the area of VA financing. Helping our distinguished Military vets is one of the most satisfying parts of what I do!
I'm available 365 days a year to help you make your dreams of home ownership a reality!
I speak with them personally and know the right questions to ask. By finding out their whole story, I can present the best options for their unique situation. You have to really understand a person's situation as well as their short and long-term goals to uncover the best possible financing for their needs.
1. Rate is everything! The worst question to ask is "what's your rate?" The goal is to acquire the best possible financing option for your individual situation. While rate is important, there are instances where the lowest rate may not give you the best possible financing package. 2. I can get a mortgage in 8 minutes online! There is no "magical mortgage". No one is going to deliver your mortgage in a day. It's a process that requires documentation and teamwork between you and a group of professionals, like my team, to provide you with the best possible home financing for your unique situation. 3. I get better rates online! There's a great deal of misrepresentation online. That 2.5% rate that you see online many times is just to get you to call. That sweetheart deal will likely disappear very quickly. And you never know what burger joint the person on the other end of the line was working at one month ago. Your mortgage is perhaps your most important financial decision. Make sure you are working with an experienced professional, like myself, who has a vested interest in making sure that you arrive "Home" in an efficient, informed manner and that you, and their referral partners, are happy and satisfied with the experience.
The most satisfying part of what I do is sitting at closing and seeing my clients and referral partners happy with their experience. Knowing that I was able to be a part of helping someone acquire their family's new home or assisting them in resolving a refinance situation, is my favorite part of what I do. It's all about making people happy!