text:
\section{Introduction}

Generating text from structured data has a variety of applications in natural language processing. Tasks such as decoding from tables \cite{lebret2016neural,sha2018order}, question answering from knowledge bases \cite{fan2019using}, and generation from RDF Triples \cite{gardent-etal-2017-webnlg}, knowledge graphs \cite{marcheggiani2018deep} and linguistic meaning representations  \cite{konstas_17} face similar challenges: interpreting structured input and writing fluent output. We focus on generating from graph structures in the form of Abstract Meaning Representations (AMR) \cite{banarescu-etal-2013-abstract}. Previous work has largely focused on generating from AMR into English, but we propose a multilingual approach that can decode into twenty one different languages.

Compared to multilingual translation, decoding from structured input
has distinct challenges. Translation models take natural language
input and must faithfully decode into natural language
output. However, as shown in \citet{zhao2020bridging}, bridging the gap between
structured input and linear output is a difficult task. In
addition, in structured input such as graphs, the input is
usually semantically under-specified. For example, in AMRs, function
words are missing and tense and number are not given. Thus, generation
from structured input must bridge the gap between (i) structure and
string and (ii) underspecified input and fully specified
output. Multilinguality brings a third challenge --- that of
generating in languages that have varied morphological and word
order properties.

Annotating natural language with AMR is a complex task and training datasets only exist for English\footnote{AMR datasets from the LDC
  can be found at
  \href{https://amr.isi.edu/download.html}{https://amr.isi.edu/download.html}}, so
previous work on AMR-to-text generation has overwhelmingly focused on
English.
We create training data for multilingual AMR-to-Text models, by taking the \textsc{europarl} multilingual corpus
and automatically annotating the English data with
AMRs using the \texttt{jamr} semantic parser. We then use the English AMRs as
the input for all generation tasks.
To improve quality, we leverage recent advances in natural language processing such as cross-lingual embeddings, pretraining and multilingual learning.
Cross-lingual embeddings have shown striking improvements on a range
of cross-lingual natural language understanding tasks
\cite{devlin-etal-2019-bert,conneau2019unsupervised,wu-dredze-2019-beto,pires-etal-2019-multilingual}.
Other work has shown that the pre-training and fine-tuning approaches
also help improve generation performance
\cite{dong2019unified,song2019mass,lawrence-etal-2019-attending,Rothe2019LeveragingPC}.
Finally, multilingual models, where a
single model is trained to translate from multiple source languages
into multiple target languages, are achieving increasingly better
results in machine translation
\cite{johnson2017google,firat2017multi,aharoni-etal-2019-massively,arivazhagan2019massively}.

By combining these techniques, we demonstrate that fluent generation is
possible for multilingual AMR-to-Text models. We use automatic and human evaluation to assess performance on (1)
\textsc{europarl} data with silver English-centric AMR as input and the 21
\textsc{europarl} languages as target and (2) on \textsc{ldc2015E86} data
with gold English-centric AMR as input and English, Spanish, Italian, and German as
target. Our results
demonstrate, for the first time, that it is possible to generate from
AMRs into multiple languages.
We show that multilingual models have
strong performance compared to single-language baselines and produce fluent output, based on the judgments of
native speakers. We further investigate how factors, such as differences in the size of the training data,
differences in language word order and morphological properties,
and differences in the set of languages used for training many-to-one
models, impact
results. We will make code and models available, to aid research in multilingual AMR-to-Text
Natural Language Generation.

\begin{figure*}[t]
    \centering
    \includegraphics[width=0.8\linewidth]{amr.png}
    \caption{\textbf{Generating into Multiple Languages from English AMR.}}
    \label{fig:pf}
\end{figure*}

\section{Related Work}

\paragraph{AMR-to-Text Generation.}
Initial work on AMR-to-text generation adapted methods from statistical machine translation (MT) \cite{pourdamghani-etal-2016-generating}, grammar-based generation \cite{mille-etal-2017-forge}, tree-to-string transducers \cite{flanigan-etal-2016-generation}, and inverted semantic parsing \cite{lampouras-vlachos-2017-sheffield}. Neural approaches explored sequence-to-sequence models where the AMR is linearized \cite{konstas_17} or modeled with a graph encoder \cite{marcheggiani2018deep,damonte_naacl18,ribeiro-etal-2019-enhancing,song-etal-acl2018,zhu-etal-2019-modeling}. As professionally-annotated AMR datasets are in English, all this work focuses on English.

One exception is the work of \citet{sobrevilla-cabezudo-etal-2019-back} which uses automatic translation to translate the English text of the LDC AMR data into Brazilian Portuguese and align  English with the Portuguese translation to create Portuguese-centric AMRs. However, this work focuses only on one language. In contrast, we consider generation into twenty one languages. We use very different methods and generate from English-centric AMRs, not target-language AMRs.

\paragraph{Multilingual MR-to-Text Generation.} While work on AMR-to-Text generation has mostly focused on generation into English, the Multilingual Surface Realization shared tasks \citep{mille2018overview,mille2019overview} have made parallel MR/Text datasets available for 11 languages.
Two tracks are proposed: a shallow track where the input is an unordered, lemmatized dependency tree and a deep track where the dependency tree edges are labelled with semantic rather than syntactic relations and where function words have been removed.

The participants approaches to this multilingual generation task use
gold training data and mostly focus on the shallow track where the
input is an unordered lemmatized dependency tree and the generation
task reduces to linearization and morphological realization. The
models proposed are pipelines that model each of these subtasks and
separate models are trained for each target language
\cite{kovacs-etal-2019-bme,yu-etal-2019-imsurreal,shimorina-gardent-2019-loria,shimorina-gardent-2019-surface,castro-ferreira-krahmer-2019-surface}. In this work, we focus instead on more abstract, deeper, input (AMRs)  and propose
end-to-end, multilingual models for all target languages.

\section{Method}

To generate from AMRs, we use neural sequence to sequence models that model the input AMR with a Transformer Encoder and generate natural language with a Transformer Decoder. For all languages, the input is an English-centric AMR that was derived automatically  using the \texttt{jamr} semantic parser from English text. We pre-train both the AMR encoder and the multilingual decoder and we leverage crosslingual embeddings.

\begin{figure}[t]
    \centering
    \includegraphics[width=0.8\linewidth]{architecture.png}
    \caption{\textbf{One-to-Many Architecture for Multilingual AMR-to-Text Generation.} The English-centric AMR input is linearized and modeled with graph embeddings with a pre-trained Transformer Encoder. Text is generated with a pre-trained Transformer Decoder initialized with cross-lingual embeddings.}
    \label{fig:architecture}
\end{figure}

\subsection{Encoding English AMR}

Abstract Meaning Representations are semantic representations that take the form of a rooted, directed acyclic graph. AMR abstracts away syntax such that sentences with similar meanings have similar AMR graphs. Full detail is not kept by the AMR --- for example, elements such as verb tense are lost. While we focus on decoding from AMR input, the structured form is reflective of other structured inputs used in tasks such as generating from semantic role labels \cite{fan2019strategies} or RDF triples \cite{gardent-etal-2017-webnlg}.

The AMR graph is first linearized into a sequence of tokens as shown in Figure~\ref{fig:pf} after preprocessing following \cite{konstas_17} (see Section 4.1 for a detailed description). Rather than model the graph structure directly, following \citet{fan2019using}, we model the graph using a \textit{graph embedding}. The graph embedding provides additional information to the Transformer Encoder by encoding the depth of each node in the rooted graph and the subgraph each node belongs to. Concretely, each token has a word and position embedding, and additionally an indicator of depth calculated from the root and an indicator of which subtree the node belongs to (with all subtrees stemming from the root).  These additional embeddings are concatenated to the word and position embeddings. Such information allows the Transformer Encoder to capture some graph structure information, while still modeling a sequence. This is depicted in Figure~\ref{fig:architecture}.

To create a one-to-many multilingual model, we model a \textit{language embedding} on the encoder side to allow the decoder to distinguish which language to generate into. This technique has been previously used in multilingual translation \cite{arivazhagan2019massively}. The English AMR begins with a token that indicates the decoder side language.

To improve the quality of the encoder, we incorporate large-scale pretraining on millions of sequences of AMR by adopting the generative pretraining approach proposed in \citet{lewis2019bart}. This pretraining incorporates various noise operations, such as masking \cite{devlin-etal-2019-bert}, span masking \cite{fan2019using}, and shuffling. Previous work has shown that pretraining is effective for providing neural models with additional information about the structure of natural language and improving model quality \cite{dong2019unified,song2019mass,lawrence-etal-2019-attending}. As models increase in size, smaller training datasets (such as human-annotated AMR) are often not large enough to fully train these models. The entire encoder is pretrained on silver AMRs, as shown in Figure~\ref{fig:architecture}.

\subsection{Multilingual Decoding from AMR}

The Transformer Decoder attends to the encoded English AMR, a graph of
concepts and relations, and generates text into many different
languages with varied word order and morphology.

As displayed in Figure~\ref{fig:architecture}, we use both language model pretraining and crosslingual embeddings to improve decoder quality.  Monolingual data from various languages is used to pretrain each language model. Further, we incorporate crosslingual embeddings. These embeddings aim to learn universal representations that encode sentences into shared embedding spaces. Various recent work in crosslingual embeddings \cite{conneau2019cross} show strong performance on other multilingual tasks, such as \textsc{xnli} \cite{conneau2018xnli}, \textsc{xtreme} \cite{hu2020xtreme}, and \textsc{mlqa} \cite{lewis2019mlqa}. We use the embeddings from XLM \cite{conneau2019cross} to initialize the multilingual embeddings of our decoder.

\subsection{Model Training}

To train our one-to-many multilingual AMR-to-text generation model, we use pairs of English AMR and text in multiple different languages. The English AMR does not need to be aligned to sentences in multiple languages. Instead, we create one AMR-to-text corpus for each language and concatenate all of them for training a multilingual model. During the training process, the pretrained AMR encoder and pretrained crosslingual decoder are finetuned on our multilingual AMR-to-text training corpus.

\section{Experimental Setting}
We describe the various sources of data used to create multilingual AMR-to-text generation models and describe the implementation and evaluation.
\begin{table*}[htbp]
\centering \small
\begin{tabular}{llllllllllll}
\toprule
\bf Model & \bf en & \bf da & \bf de & \bf el & \bf es & \bf fi & \bf fr & \bf it &  \bf nl & \bf pt & \bf sv \\
Amount of Data & 8.2M & 1.9M & 1.9M & 1.2M & 1.9M & 1.9M & 2M & 1.9M & 1.9M & 1.9M &  1.9M\\
\midrule
Machine Translation & --- & --- & \bf 17.8 & --- & 24.9 & --- & \bf 20.7 & 18.6 & 19.4 & 21.0 & 19.2 \\
\midrule
English AMR-XX & \bf 34.2 & 21.3 & 16.9 & 14.2 & 24.3 & 12.9 & 20.5 & \bf 19.1 & 18.8 & 20.4 & 18.6 \\
\midrule
Multilingual & 32.5 & 21.2 & 17.0 & 13.8 & 24.2 & 12.4 & 19.7 & 17.8 & 18.5 & 20.5 & 18.7 \\
+ Graph Embedding & 32.9 & 21.4 & 17.0 & 14.0 & 24.3 & 12.5 & 19.9 & 18.0 & 18.6 & 20.7 & 18.9 \\
+ Crosslingual Embedding & 33.0 & 21.7 & 17.3 & 14.4 & 24.7 & 12.9 & 19.9 & 18.5 & 19.0 & 21.0 & 19.0 \\
+ Encoder Pretraining & 33.4 & 21.7 & 17.3 & 14.5 & 24.9 & 13.0 & 20.2 & 18.7 & 19.1 & 21.0 & 19.1 \\
+ Decoder Pretraining & 33.8 & \bf 21.9 & 17.5 & \bf 14.6 & \bf 25.1 & \bf 13.4 & 20.3 & 18.9 & \bf 19.4 & \bf 21.2 & \bf 19.5 \\
\bottomrule
\toprule
\bf Model & & \bf bg & \bf cs & \bf et & \bf hu & \bf lt & \bf lv & \bf pl & \bf ro & \bf sl & \bf sk \\
Amount Data & & 400K & 650K & 650K & 620K & 630K & 640K & 630K & 400K & 640K & 620K \\
\midrule
English AMR-XX & & 33.8 & 27.5 & 18.9 & 23.1 & 23.9 & 25.4 & 23.4 & 30.6 & 30.1 & 28.7 \\
\midrule
Multilingual & & 34.6 & 28.4 & 19.1 & 23.8 & 24.4 & 26.9 & 23.4 & 31.5 & 30.6 & 29.7 \\
+ Graph Embedding & & 34.7 & 28.5 & 19.3 & 23.9 & 24.5 & 27.0 & 23.6 & 31.5 & 20.7 & 29.9 \\
+ Crosslingual Embedding & & 35.0 & 28.9 & 19.7 & 24.3 & 24.8 & 27.4 & 24.0 & 31.7 & 30.8 & 30.1 \\
+ Encoder Pretraining & & 35.2 & 29.0 & 19.8 & 24.5 & 25.0 & 27.5 & 24.1 & 31.9 & 31.0 & 30.2 \\
+ Decoder Pretraining & & \bf 35.7 & \bf 29.5 & \bf 21.2 & \bf 24.7 & \bf 25.5 & \bf 27.9 & \bf 24.4 & \bf 32.1 & \bf 31.4 & \bf 30.6 \\
\bottomrule
\end{tabular}
\caption{\label{table:full_results} \textbf{Results on 21 Languages in Europarl}. The English-XX baseline (generation into a single language) combines all modeling improvements. When training on multiple seeds, the standard deviation is around 0.1 to 0.3 BLEU, making the difference between the multilingual baseline and the addition of our modeling improvements statistically significant.}
\end{table*}

\subsection{Data}

\paragraph{Pretraining} For encoder pretraining on silver AMR, we take thirty million sentences from the English portion of \textsc{ccnet}\footnote{\url{https://github.com/facebookresearch/cc_net}} \cite{wenzek2019ccnet}, a cleaned version of Common Crawl (an open source version of the web). We use \texttt{jamr}\footnote{\url{https://github.com/jflanigan/jamr}} to parse English sentences into  AMR.
For multilingual decoder pretraining, we take thirty million sentences from each language split of \textsc{ccnet}.

\paragraph{Multilingual Data} We use \textsc{europarl}, an aligned corpus of European Union parliamentary debates. Each language in \textsc{europarl} is aligned to English. We study the twenty one languages available in \textsc{europarl}: Bulgarian, Czech, Danish, Dutch, English, German, Greek, Spanish, Estonian, Finnish, French, Hungarian, Italian, Lithuanian, Latvian, Polish, Portuguese, Romanian, Slovak, Slovenian, and Swedish. The earliest releases in \textsc{europarl} were prepared with a fixed common testing set across all languages, but later releases in ten new languages do not have a validation or test set. Thus, for the languages where the standard split is applicable, we report results on the common testing set, splitting it in half for validation and testing. For languages where there is no evaluation set, we take a part of the training set and reserve it for validation and another portion for testing. We use \texttt{jamr} to parse the English text of the Europarl corpus into AMRs. This creates a corpus of automatically created silver English AMRs aligned with sentences in twenty one European languages.

\paragraph{Gold AMR}
We also evaluate our models (trained on silver AMRs) on gold AMR where available. For this, we use the \textsc{crosslingual amr} dataset from \citet{cohen2018cross}\footnote{To evaluate on this data, please contact \citet{cohen2018cross}}.
The corpus was constructed by having professional translators translate the English text of the \textsc{ldc2015E86} test set into Spanish, Italian, German, and Chinese. We only evaluate on languages where we have training data from \textsc{europarl} (i.e. we do not include Chinese as it is not in \textsc{europarl}).

\paragraph{Preprocessing}
All data remains untokenized and cased. For AMR, we follow \citet{konstas_17} in processing the \texttt{jamr} output into a simpler form. We remove variable names and instance-of relation ( / ) before every concept. However, we do not anonymize entities or dates, as improvements in modeling have allowed for better representations of rare words such as entities. We learn a sentencepiece model with 32K operations to split the English AMR into subword units. On the decoder side, we apply the sentencepiece model and vocabulary of XLM \cite{conneau2019cross}. We choose to use the existing XLM sentencepiece and vocabulary so that the XLM cross-lingual embeddings can be used to initialize our models. For the encoder, we do not use existing vocabularies, as they do not capture the AMR graph structure.
\subsection{Models}
We implement our models in \texttt{fairseq-py} \cite{ott2019fairseq}. We use large Transformer \cite{vaswani2017attention} sequence-to-sequence models and train all models for 50 epochs with LayerDrop \cite{fan2019reducing}, which takes around 2 days. We initialize all weights with the pretrained models. When combining crosslingual word embeddings and encoder and decoder pretraining, we initialize all weights with pretraining, then use  crosslingual word embeddings. We do not perform extensive hyperparameter search, but experimented with various learning rate values to maintain stable training with pretrained initialization. To generate, we decode with beam search with beam size 5. Our pretrained models are available for download.\footnote{\url{https://github.com/facebookresearch/m-amr2text}}
\subsection{Monolingual and Translation Baselines}
We compare our multilingual models both to monolingual models (one
model trained for each language) and to a hybrid NLG/MT baseline. For
the latter, we first generate with the AMR-to-English model and then
translate the generation output to the target language using MT. Our
translation models are Transformer Big models trained with LayerDrop
\cite{fan2019reducing} for 100k updates on public benchmark data from
WMT where available and supplemented with mined data from the
\texttt{ccMatrix} project \cite{schwenk2019ccmatrix}. We trained translation models for languages where large quantities of aligned bitext data are readily available, and cover a variety of languages.

\begin{table}[!h]
\centering\small
\begin{tabular}{lllll}
\toprule
\bf Model & \bf en & \bf es & \bf it & \bf de \\
\midrule
Konstas et al. (2017) & 22.0 & --- & --- & --- \\
Song et al. (2018) & 23.3 & --- & --- & --- \\
Cao et al. (2019) & 23.5 & --- & --- & --- \\
Damonte et al. (2019) & 24.4 & --- & --- & --- \\
Guo et al. (2019) & 25.7 & --- & --- & --- \\
Ribeiro et al. (2019) & 24.3 & --- & --- & --- \\
Zhu et al. (2019) & \bf 29.7 & --- & --- & --- \\
\midrule
Machine Translation & --- & 21.6 &  19.6 & \bf 15.7 \\
\midrule
English-XX Seq2Seq & 25.2 & 21.1 & \bf 19.8 & 14.9 \\
\midrule
Multilingual Seq2Seq & 24.2 & 21.0 & 19.0 & 14.7 \\
+ Graph Attribute & 24.5 & 21.0 & 19.2 & 14.8 \\
+ Crosslingual Embed & 24.6 & 21.3 & 19.4 & 15.1 \\
+ AMR Enc Pretrain & 24.7 & 21.5 & 19.6 & 15.1 \\
+ Multiling Dec Pretrain & 24.9 & \bf 21.7 & \bf 19.8 & 15.3 \\
+ Finetune on Gold AMR & 26.3 & --- & --- & --- \\
\bottomrule
\end{tabular}
\caption{\label{table:gold_results} \textbf{Results on Gold AMR from LDC2015E86}.}
\end{table}

\begin{table*}[htbp]
\centering\small
\begin{tabular}{lccccc|cc}
\toprule
 & \bf Number of & & \bf Word & \bf Semantic & \bf Good & \bf Std Dev & \bf Std Dev \\
\bf Language & \bf Evaluators & \bf Morphology & \bf Order &  \bf Accuracy & \bf Paraphrases & \bf Morphology & \bf Word Order \\
\midrule
English & 7 & 2.9 & 2.9 & 2.4 & 84\% & 0.06 & 0.04 \\
Danish & 2 & 2.9 & 2.9 & 2.3 & 88\% & 0.09 & 0.04 \\
German & 4 & 3.0 & 2.9 & 2.2 & 75\% & 0.02 & 0.06 \\
Greek & 5 & 2.9 & 2.9 & 2.2 & 75\% & 0.06 & 0.04 \\
Spanish & 10 & 2.9 & 2.9 & 2.2 & 81\% & 0.09 & 0.07 \\
Finnish & 2 & 2.9 & 3.0 & 2.1 & 69\% & 0.01 & 0.00 \\
French & 7 & 3.0 & 3.0 & 2.3 & 81\% & 0.02  & 0.03  \\
Italian & 5 & 3.0 & 3.0 & 2.3 & 82\% & 0.04 & 0.05 \\
Dutch & 7 & 2.9 & 2.9 & 1.9 & 60\% & 0.06 & 0.06 \\
Portuguese & 7 & 2.9 & 2.9 & 2.4 & 83\% & 0.08 & 0.06 \\
Swedish & 5 & 2.9 & 2.9 & 2.3 & 84\% & 0.04 & 0.08 \\

Bulgarian & 6 & 2.8 & 2.8 & 2.0 & 67\% & 0.07 & 0.11 \\
Czech & 3 & 2.9 & 2.8 & 2.3 & 79\% & 0.05 & 0.11 \\
Estonian & 1 & 2.9 & 2.9 & 2.2 & 78\% & --- & --- \\
Hungarian & 5 & 2.6 & 2.5 & 2.1 & 70\% & 0.14 & 0.23 \\
Latvian & 3 & 2.8 & 2.7 & 2.1 & 74\% & 0.07 & 0.16 \\
Polish & 2 & 2.8 & 2.9 & 1.6 & 54\% & 0.10  & 0.04\\
Romanian & 10 & 2.7 & 2.7 & 1.9 & 68\% & 0.22 & 0.23 \\
\bottomrule
\end{tabular}
\caption{\label{table:human_eval} \textbf{Human Evaluation}. Native speakers assess fifty sentences on a scale of 1 to 3, with 3 the highest score. Good Paraphrases are sentences with high scores (2 or 3) for both Semantic Accuracy and Paraphrasing.}
\end{table*}

\subsection{Evaluation} We evaluate with detokenized BLEU using \texttt{sacrebleu} \cite{post-2018-call}. We conduct human evaluation by asking native speakers to evaluate word order, morphology, semantic faithfulness (with respect to the reference) and paraphrasing (how much the generation differs from the reference) on a 3 point scale. The evaluation was done online. For each language, evaluators annotated 25 test set sentences with high BLEU score and 25 sentences with low BLEU score. We removed sentences that were shorter than 5 words. As it is difficult to ensure high quality annotations for 21 languages using crowdsourcing, we relied on colleagues by reaching out on NLP and Linguistics mailing lists. As a result, the number of evaluators per language varies (cf. Table \ref{table:human_eval}).

We evaluate multilingual AMR-to-Text generation models in 21
languages. We conduct an ablation study which demonstrates the
improvements in modeling performance induced by incorporating graph
embeddings, cross lingual embeddings, and pretraining. Finally, we
analyze model performance with respect to several linguistic attributes (word order, morphology, paraphrasing, semantic faithfulness) using both automatic metrics and human evaluation.

\subsection{Multilingual AMR-to-Text Generation}

\paragraph{Monolingual vs. Multilingual Models.}
We compare English-XX baselines trained to generate from AMR into a single language with multilingual models. We note that as the English-XX models specializes for each language, they have less to model with the same parameter capacity.
Results are shown in Table~\ref{table:full_results}.
Overall, multilingual models perform well --- on 18 of the 21 languages, the performance measured by BLEU is stronger than the monolingual baseline.

One advantage of multilingual AMR-to-Text generation is increased quantities of AMR on the encoder side. This is particularly helpful when the size of the training data is low. For instance, Estonian (\textit{et}) sees a 2.3 BLEU point improvement from multilingual modeling.
Conversely, languages such as English, Swedish and French
benefit less from multilingual modeling, most likely because there is sufficient data for those languages already.
More generally, there is a marked difference between languages for which the training data is large and those for which the training data is smaller. When the training data is large (1.9 to 2M training instances, top part of Table~\ref{table:full_results}), the average improvment is +0.36 BLEU (Min:-0.2, Max:+0.9) whereas for languages with smaller training data (400 to 620K training instances, bottom part of Table~\ref{table:full_results}\footnote{For many languages, such as Slavic languages, it is because the EU expanded to include these countries later on. Thus there is less European Parliamentary proceeding data.}), the average improvement is +1.75 (Min:+1, Max:+2.3).  These trends are similar to observations on other tasks --- namely that pretraining is most helpful when there is not sufficient training data in the task itself to train strong representations.

\section{Results}

\paragraph{Performance on Gold English AMR}
We evaluate our models trained on silver AMR on the
\textsc{crosslingual amr} dataset from \citet{cohen2018cross} where the input is a gold English-centric AMR and the output is available in three European languages: Spanish, French, and Italian.
The results are shown in Table~\ref{table:gold_results}. Similar to the trends seen when generating from silver AMR, we find that multilingual models have strong performance. BLEU scores are lower than on \textsc{europarl} as the models are tested out of domain (training on parliamentary debates but testing on newswire and forum data domains).

On English LDC data, we compare to existing work. Even though it is trained on silver AMRs and out of domain, non-LDC data, the multilingual model compares well with previous work (see Table~\ref{table:gold_results}).
When finetuned on the \textsc{ldc2015E86} train set, our model improves on English by over 1 BLEU point, outperforming all previous work except \citet{zhu-etal-2019-modeling}. This work directly models the graph structure of AMR with structure aware attention to improve Transformer architectures --- this is orthogonal to our main aim of multilingual generation and can be incorporated in future work.

\paragraph{Impact of Modeling Improvements.} For the multilingual model, we display the effect of incrementally adding additional modeling improvements (cf. Table~\ref{table:full_results}). Each improvement is essentially universally helpful across all considered languages, though some have a greater improvement on performance than others.

\begin{table*}[htbp]
\centering\small
\begin{tabular}{lccc}
\toprule
\bf Evaluation & \bf Morphology & \bf Word Order & \bf Semantic Accuracy \\
\midrule
\bf Spanish & \\
Machine Translation & 2.9 & 2.7 & 2.0 \\
Multilingual AMR to Text & 2.8 & 2.9 & 2.1 \\
\midrule
\bf Italian & \\
Machine Translation & 3.0 & 2.9 & 2.2 \\
Multilingual AMR to Text & 2.9 & 3.0 & 2.1 \\
\midrule
\bf German & \\
Machine Translation & 2.8 & 2.9 & 2.0 \\
Multilingual AMR to Text & 3.0 & 3.0 & 2.2 \\
\bottomrule
\end{tabular}
\caption{\label{table:human_eval_mt} \textbf{Human Evaluation of our approach compared to the Hybrid English AMR to Text + Machine Translation baseline using Gold AMR from LDC2015E86}. Two native speakers per language assess fifty sentences each on a scale of 1 to 3, with 3 being the highest score.}
\end{table*}
\begin{table*}[htbp]
\centering \small
\begin{tabular}{lll}
\toprule
\bf English & \bf Generation & This point will certainly be the subject of subsequent further debates in the council. \\
& \bf Reference & This is a point that will undoubtedly be discussed later in the Council.
\\
\\
\bf French & \bf Generation & Je ne suis pas favorable \`{a} des exceptions  \`{a} cette r\`{e}gle.
\\
& \bf Reference & A mon avis, il n'est pas bon de faire des exceptions  \`{a} cette r\`{e}gle.
\\
\\
\bf Swedish & \bf Generation & Därför röstade vi inte för detta betänkande.
\\
& \bf Reference & Vi har därför inte röstat för detta betänkande.
\\
\bottomrule
\end{tabular}
\caption{\label{table:generation_examples} \textbf{Example Paraphrases} generated by our multilingual model.}
\end{table*}

\paragraph{Comparison to the Hybrid NLG/MT Baseline.}
Compared to the NLG/MT baseline, our multilingual models provide comparable results while providing an arguably simpler approach (end-to-end rather than pipeline) and training on much lower quantities of parallel data --- on German and French (very high resource languages with millions of examples of training data), there is slightly stronger performance. On other languages we compare to, the translation models perform a bit worse.
\begin{table}[htbp]
\centering\small
\begin{tabular}{llllll}
\toprule
\bf Model & \bf es & \bf fr & \bf it & \bf pt & \bf ro \\
One Language & 25.2 & 20.3 & 18.9 & 22.2 & 32.1 \\
Romance Family & 25.5 & 20.5 & 19.3 & 22.5 & 32.5 \\
All Languages & 25.3 & 20.5 & 19.3 & 22.4 & 32.2 \\
\midrule
 &  &  \bf da & \bf de & \bf nl & \bf sv \\
One Language & & 21.3 & 17.0 & 18.5 & 18.7 \\
Germanic Family & & 21.8 & 21.9 & 19.6 & 19.3 \\
All Languages & & 21.9 & 17.5 & 19.4 & 19.5 \\
\bottomrule
\end{tabular}
\caption{\label{table:language_families} \textbf{Performance when training with increasingly more languages.} Training one multilingual AMR-to-Text model with languages in the related language family improves performance.}
\end{table}

We further conduct a human evaluation study on Spanish, Italian, and German. We ask evaluators to assess the morphology, word order, and semantic accuracy of our Multilingual AMR to Text system compared to this hybrid English AMR to Text + Machine Translation baseline. We show in Table~\ref{table:human_eval_mt} that the two models score very similarly in human evaluation, indicating the strength of this fully multilingual system in producing fluent output.

\subsection{Analysis of Multilingual Generation}

A core challenge for multilinguality is that languages
differ with respect to word order and morphology, so models must learn this per language. We use
automatic and human evaluation to investigate how these
differences affect performance.

\paragraph{Morphology}

\begin{figure}[ht]
    \centering
    \includegraphics[width=\columnwidth]{alignment.png}
    \caption{\textbf{Attention alignment} when decoding in French and German from the same input AMR.}
    \label{fig:alignment}
\end{figure}
\begin{figure}[ht]
    \centering
    \includegraphics[width=\columnwidth]{type_token.png}
    \caption{\textbf{Relationship between BLEU Score and Token Overlap} for all 21 languages. Correlation coefficient between word overlap and BLEU is 0.42, and coefficient between subword overlap and BLEU is 0.26.}
    \label{fig:type_token}
\end{figure}

Instead of operating on words, our models use sentencepiece \cite{wu2016google}, a data-driven approach to break
words into subwords. As shown in \citet{wu-dredze-2019-beto}, in
transfer-based approaches to natural language understanding tasks, the proportion of subwords
shared between the source and the transfer language impacts
performance. We therefore explore the relation between the proportion of subwords and words shared between the AMR and the output vocabulary. Figure~\ref{fig:type_token} displays this relationship, with weak positive correlation for both word and subword overlap.

We further assess morphology by asking human evaluators to grade the
morphology of sentences (\textit{Is the morphology correct? Are agreement
constraints e.g., verb/subject, noun/adjective respected?}) on a
scale from 1 to 3 with 3 being the highest score.
As Table~\ref{table:human_eval} shows, there is not much difference in performance between languages even
though there is a marked difference in terms of agreement constraints
between e.g., Finnish and English. Between annotators, agreement was high --- the standard deviation across  was low, with the exception of Romanian, Hungarian, and Spanish (as shown in Table~\ref{table:human_eval}). This demonstrates the
surprisingly high ability of multilingual models to
generalize across languages.

\paragraph{Word Order}
To assess the impact of varied word orders by language, we
ask human evaluators to judge if
the word order is natural. As shown in
Table~\ref{table:human_eval}, for all languages except Latvian and
Romanian, the score is very high (close to 3) indicating that the model
 learns to decode into multiple languages even though
word order differs. The agreement between annotators was high, with low standard deviation (see Table~\ref{table:human_eval}). Further, the attention pattern between the
encoder English AMR and the decoder clearly
reflects the word order of the various languages. This is illustrated in Figure~\ref{fig:alignment}, where the activation pattern mirrors the word order difference between French (\ref{ex:frge-fr}) and German (\ref{ex:frge-ge}).

\enumsentence{
  \textit{ont tenu (une r\'{e}union de groupe)$\mathrm{_{OBJ}}$ (en Janvier 2020)$\mathrm{_{TIME}}$ (\`{a} New York)$\mathrm{_{LOC}}$}\\ \label{ex:frge-fr}\vspace{-0.75cm}
}
\enumsentence{{\textit{hielten (im Januar 2020)$\mathrm{_{TIME}}$ (in New York)$\mathrm{_{LOC}}$ (eine Gruppestreffen)$\mathrm{_{OBJ}}$} \label{ex:frge-ge}}
}

\paragraph{Training on Related Languages}

Multilingual models have the potential to benefit from similarities between languages. Languages of the same family often have shared morphological characteristics and vocabulary. First, we analyze the performance of training on languages within a family. Table~\ref{table:language_families} displays that a model trained on languages within a family has the strongest performance.

Second, we analyze languages within the same family. For four families: Romance, Germanic, Uralic, and Slavic, we create multilingual models trained on pairs. One pair is for the most related languages within that family (e.g. Spanish and Portuguese) and another pair is for the farthest languages within that family (e.g. Spanish and Romanian). We determine which pairs are close and far from \citet{ahmad2019difficulties}. Results in Figure~\ref{fig:pairs} display that training on pairs of closely related languages has better performance than pairs of less closely related languages, even within a family. Multilingual models could pick up on similarities between languages to improve performance.

\begin{figure}[t]
    \centering
    \includegraphics[width=\columnwidth]{pairs.png}
    \caption{\textbf{BLEU difference training on Close v. Far Languages within One Family}. Training on a close pair consistently improves performance compared to training on a far pair, even within a language family.}
    \label{fig:pairs}
\end{figure}

\paragraph{Semantic Accuracy and Paraphrasing.}
We ask human evaluators to grade the faithfulness of the hypothesis compared to the reference on a scale of 1 to 3. As shown in Table~\ref{table:human_eval}, the overall semantic accuracy is very high (note a score of 2 indicates \textit{minor differences}). We also asked annotators to evaluate how different the generated sentence was from the reference. When coupled with the semantic accuracy score, this allows us to evaluate generation of true paraphrases i.e., sentences with the same meaning as the reference but different surface form. In Table~\ref{table:human_eval}, \textit{Good Paraphrases} indicates the percentage of cases that scored highly (2 or 3) with respect to both semantic adequacy and paraphrasing.
A large majority of generated sentences are labeled as valid paraphrases by native speakers, indicating (i) that despite underspecified input, the written sentence retains the meaning of the reference and (ii) that this underspecification allows for the generation of paraphrases. This also suggests that BLEU scores only partially reflect model performance as good paraphrases typically differ from the reference and are likely to get lower BLEU score even though they may be semantically accurate. Table~\ref{table:generation_examples} shows some examples illustrating the paraphrasing potential of the approach.

\section{Conclusion}

Abstract Meaning Representations were designed to describe the meaning of English sentences. As such they are heavily biased towards English. AMR concepts are either English words, PropBank framesets (``want-01'') or special, English-based keywords (e.g., ``date-entity''). The structure of AMRs is also influenced by English syntax. For instance, the main relation of \textit{``I like to eat''} is the concept associated with its main verb (``like'') whereas given the corresponding German sentence \textit{"Ich esse gern'' (Lit. ``I eat willingly''}), the main predicate might have been chosen to be ``eat'' (``essen''). In other words, AMRs should not necessarily be viewed as an interlingua \cite{banarescu-etal-2013-abstract}. Nonetheless, our work suggests that it can be used as one: given an English-centric AMR it is possible to generate the corresponding sentence in multiple languages. This is in line with previous work by \cite{damonte_naacl18} which shows that despite translation divergences, AMR parsers can be learned for Italian, Chinese, German and Spanish which all map into an English-centric AMR.

\section*{Acknowledgments}

We thank the anonymous reviewers for their feedback. We gratefully acknowledge the support of the French National Research Agency (Gardent; award ANR-20-CHIA-0003, XNLG "Multi-lingual, Multi-Source Text Generation").

\bibliographystyle{acl_natbib}

redpajama_set_name: RedPajamaArXiv


Shop wide selection of storage and organization products for your home at low discount prices. Get the best prices on home storage solutions such as bath accessories, kitchen organizations, closet organizers, office organizers and more at StacksAndStacks.com. Latest Stacks And Stacks coupons below.


text:
La barra de tasques és un element de la interfície gràfica d'usuari que té diversos propòsits. Pot mostrar els arxius i les carpetes que estan obertes o els programes que s'estan executant. A causa de la seva prominència a la pantalla, la barra de tasques normalment també té una àrea de notificació, que utilitza icones interactives per a mostrar informació en temps real sobre l'estat del sistema a l'ordinador, l'idioma instal·lat o l'hora segons el fus horari escollit per l'usuari. L'ús d'escriptoris virtuals evita la saturació de la barra de tasques. El disseny de la barra de tasques varia entre els diferents sistemes operatius, però generalment assumeix la forma d'una tira situada al llarg d'una vora de la pantalla.

Al Windows 1.0 ja hi havia una barra que mostrava diverses icones que corresponien als programes que l'usuari estava executant. En fer clic en aquestes icones, l'usuari podia canviar fàcilment entre programes. El Windows 95 va ser la primera versió amb menú inici i que admetia el desplaçament de la barra de tasques.

Referències

Interfície gràfica d'usuari

redpajama_set_name: RedPajamaWikipedia


text:
RV Rental
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Explore Attractions in Canada
Looking for sights to see and things to do in Canada? Explore our list of attractions and experiences available. If you already know where you're going, simply select the state to view its attractions.
Select State Choose... Alberta British Columbia Manitoba Nova Scotia Ontario Quebec Yukon Territory
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Aerospace Museum of Calgary
Alberta Calgary
Learn all about Western Canada's aviation history at Aero Space Museum of Calgary. The city's first aviation museum is located in a helicopter hangar...
Alexander Keiths Brewery
Nova Scotia Halifax
Alexander Keith's Brewery was founded by Alexander Keith in 1820 in Halifax, and is presently one of the oldest commercial breweries in North America...
Assiniboine Park
Manitoba Winnipeg
Assiniboine Park is said to be the next Central Park - or maybe even surpass what New York has to offer! It was established in 1904, covering...
Banff National Park was established in 1885, making it the oldest national park in Canada. It is known for its lovely meadows, dense forests,...
Calaway Park
Calaway Park has been the largest outdoor family amusement park in Western Canada since 1982 and continues to offer great entertainment for all ages....
At 191 metres (626 feet), the Calgary Tower can easily be spotted despite being surrounded by several high-rise buildings. The Tower played a special...
Canada Olympic Park
The success of the 1988 Winter Olympics paved the way for another patronized tourism spot in Calgary – the Canada Olympic Park. It is currently used...
British Columbia Vancouver
Located just minutes from downtown Vancouver, Capilano Suspension Bridge gives delight to adventure-seeking guests. It was originally made out of...
Ontario Toronto
Casa Loma is truly a sight to behold in midtown Toronto. The Gothic Revival style castle was advanced for its time, as it had several technological...
Dr Sun Yat-Sen Chinese Garden
Dr. Sun Yat-Sen Classical Chinese Garden is the first full-size Chinese garden built outside of China. The garden applies the principles of Feng Shui...
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redpajama_set_name: RedPajamaCommonCrawl


In the event that a bone is broken due to someone else's negligence, the injured person can make a claim to recover money to compensate for damages from the injury. The severity of the injury can vary from relatively minor to quite severe, but pain will be associated regardless of how bad the break is. Additional injury to nerves, muscles, blood vessels, organs and surrounding skin are frequent when bones are broken. If you suffered broken bones in Redlands, an experienced personal injury attorney can answer your questions and ensure that you are fairly compensated for your pain and suffering, injuries and lost time at work.
Osteomyelitis is an infection of the bone or bone marrow; inflammation in the bone due to infection. Osteomyelitis often occurs as a complication of injury or surgery. In some cases, the infection may get into bone tissue from the bloodstream. Patients with osteomyelitis typically experience deep pain and muscle spasms in the inflammation area, as well as fever.
Osteonecrosis is a disease resulting from reduced bloodflow to bones in the joints. With too little blood, the bone starts to die and may break down. Osteonecrosis is most often found in the hips, knees, shoulders, and ankles. You may have osteonecrosis in one or more bones.
Typically, a pulmonary embolism is caused by blood clots that travel from the legs or, rarely, other parts of the body (deep vein thrombosis, or DVT).
At the Law Offices of Brent A. Duque, we are committed to high quality legal representation of personal injury clients and accident victims who are worried and suffering losses. Duque Law Group truly cares about you, the patient with broken bones, and we are responsive to your needs.
We provide comprehensive legal representation and fight aggressively for our clients in difficult broken bones cases throughout the Southern California area. We have settled or litigated thousands of personal injury cases and never charge a fee unless we recover damages. Our investigators, case managers and trial attorneys can help you recover medical expenses, lost wages and restitution for pain and suffering. We collect the highest possible settlement for our personal injury clients.


text:
Home / Lighting / Wall lamps / Stella Hexagon Lamp - Roll & Hill
Stella Hexagon Lamp - Roll & Hill
Stella Hexagon Lamp - Roll & Hill quantity
PRICE 16.331€
Sizes: 41cm x 20cm x 86cm
Delivery 12 - 16 weeks
The Stella Hexagon wall lamp is a design by Rosie Li for signing Roll & Hill which is inspired by the painter, from whom it takes its name. When illuminated, the glass diffuser and mirror reveal a series of ringed geometric shapes, creating the impression of infinite volume within a finite space. Also available in triangle version.
Details: Wall lamp in bronze, mirror glass, steel and wood. LED lighting, 240 V.
Consult more information with Naharro furniture.
Roll & Hill is a high-end modern lighting and lamps firm based in New York. Always at the forefront of current design.
Roll & Hill was founded in 2010 by Jason Miller. His experience as a designer inspires him to create a company that could take advantage of the talent of new independent designers. His pieces are a mix between modern design and conceptual art.
His creations, totally innovative and groundbreaking, arise from the close collaboration with American and international creators who work from the studios that the firm has in Brooklyn and Long Island.
The different materials used are assembled carefully by hand. The clear selection of his work makes each lamp a unique work of art.
The firm is committed to the work of modern lighting systems, both for private homes and public spaces.
Naharro furniture is the official distributor of the Roll & Hill lighting brand with exhibition in its Madrid stores and online store, where you can see and buy your entire catalog.
Rosie Li was born in Zhengzhou, China, and immigrated to the US for the 3 years. In fourth grade, his art teacher discovered his creative skills when it comes to representing animals, encouraging him to continue studying drawing and painting. This interest led her to the Rhode Island School of Design at 2007, where she developed a sensitivity to materials and rational design.
Mixing graphic elements and geometric shapes, Rosie creates works that challenge visual abilities and perception. At 2015, he founded his design studio Rosie Li Studio in Brooklyn.
Naharro furniture distributes the pieces manufactured by different lighting brands designed by Rosie Li in its stores in Madrid and online store, with delivery and assembly service throughout Spain. Ask us for any additional information.
Wall lampsRosie LiRoll & Hill
× × 41 20 86 cm
Stella Triangle Lamp - Roll & Hill


Bring your antennas for testing and characterization! Marc Thorson, WB0TEM and Kent Britain, WA5VJB will once again run the VHF/UHF/Microwave antenna range with the help of Chris Petroff W5CBP. Compare your antennas to the CSVHFS standards and explore your antenna's pattern. Even if you don't bring an antenna for testing, come on out to the range and learn about how they are run and learn about antennas.
Bring your Low Noise pre-amps for testing and characterization! We'll have equipment and volunteers to test your pre-amps and transverters (for system NF). Get building!
VHF/UHF/Microwave vendors will be present to sell pieces, parts, and more.
Bring your excess equipment for trading on Friday Night. You have items of unique interest to VHF/UHF/Microwave operators and they have interest in what you have. Work out a trade!
Have content to share with the attendees but not able to work it into a paper or a presentation? Consider a "Poster Session" or a table-top display. Tri-Fold poster boards are available at Office Depot and Staples. We'll have table space for you to display them.
Bring your Rover vehicle and offer "shack visits"! We'll have a specific place for you to park on Friday morning. Conference attendees can work their way down the row to check out the many rovers, visit with their owners, etc.
Bring your portable Microwave Dish system for show-and-tell! Owners will have the opportunity to provide show-and-tell to Conference attendees. Learn how they've solved problems and integrated their systems. Operate your first 10 GHz and 24 GHz contacts with gear available for trying.
The Hospitality Room will allow for conversations and networking to continue into the late hours without worrying about disturbing the neighbors. A favorite for many attendees! Rocky Mountain Ham Radio, sponsor of the 2017 Central States VHF Society Conference, will help sponsor the snacks and beverages.
Our conference is an ARRL sanctioned event. Section, Division, and National ARRL representatives will be present.


Here at the Health Coach Group we know you invested a lot of time, money and soul into your education to become a health coach and now it's time to build your practice. We know helping people overcome health issues, giving them knowledge to live fulfilled lives, and changing the world one person at a time are the reasons why you are here.
The excellent resources presented here will help you build (as fast paced or as steady as you want) on your health coach education foundation and create the thriving practice you deserve.
Building a practice takes time, money and an entirely different skill set.
Are you overwhelmed and maybe even sick of thinking about the hundreds of steps that all takes?
It's time to heave a sigh of relief – you have come to the right place!
Here at the Health Coach Group we are committed to making sure you are doing what you do best: working with people. So we set out to create a host of free resources to make building your practice, and your dreams, a reality.
It's all free, just for you. That's actually not even the best part.
The best part: this education is ongoing and will be constantly updated as technology changes, business evolves, and new ideas are born. You will have access to a continual source of free education resources to make sure you stay at the top of your game, and your clients will notice.
So step inside, pick a topic and dive in. This is your life, this is your dream, and school is now in session.


The box office will also be open two hours before each show in the Signature Series.
The phone number for the box office is 803.407.5011.
Take I-26 W to exit 102a (Lake Murray Blvd. toward Irmo).
Turn left at the second light, onto College Street.
Harbison Theatre is approximately two blocks down on the left.
Take I-26 E to exit 102A (Lake Murray Blvd. toward Irmo).


text:
"We've had a lot of success … with our hot chicken that we run once a month," Gaudreau says of a regular special at Dempsey's. "So we decided to spin it off into its own restaurant."
Dempsey's Biscuit Co. will serve a variety of hot chicken and biscuit sandwiches.
"We're a scratch kitchen," Gaudreau says. "We don't bring in frozen product. Same as Dempsey's."
There will be a biscuit sandwich menu, most of which will feature chicken.
"We're going to be making our biscuits from scratch," Gaudreau says.
There also will be a hot chicken menu with fried chicken similar to what he serves at Dempsey's. There will be differing levels of spice on the chicken. Gaudreau says he's still working on variations with heat, including a "crazy hot" level.
There will be house specials, too.
"The whole theme of the restaurant is really … more southern-style cooking," Gaudreau says.
That includes grits, gumbo and etouffee along with brunch on Saturdays and Sundays.
"Wichita has just been a great market," Gaudreau says.
Though he lives in Lawrence now, Gaudreau grew up in College Hill and says he used to go to the East Douglas building when it was a Taco Tico.
Similarly, he used to go to Clifton Square for a candy shop that was just down from his school.
"I'd ask the teacher to use the bathroom, and I'd run out to the candy store," Gaudreau says.
"It's fun for me to come back into town and be in the Taco Tico building," he says. "I want to offer another style restaurant for the neighborhood."
He says he incorporated the Dempsey's name with the Biscuit Co. to capitalize on the restaurant's recognition in the area.
Unlike Dempsey's Burger Pub, the Biscuit Co. will have casual counter service with food runners who bring food to diners.
"It's my first time to ever own a casual-counter restaurant," Gaudreau says.
There will be 16 tables.
"With such few tables, we really need to turn really fast," Gaudreau says.
He says "the beauty of this" is there will be half the wait time for food.
"It's a math issue at this point," he says. "As long as we're busy, it makes sense to be casual-counter."
Gaudreau has six restaurants, including ones in Kansas City, Lawrence and Topeka along with the new one, which will open in January.
"Yeah, the timing's terrible," Gaudreau says of opening during such a traditionally slow month for restaurants.
"I'm hoping that this will give us a little slower opening to get our legs underneath us."
Opening has taken longer than Gaudreau planned because the deal took more time to finalize than expected, and he's been doing updating at the building as well. He says it may be for the best, though.
"I kind of like the fact that we're going to open on a slow month," he says. "It … gives us that breathing room."
If all goes well, this will not be Gaudreau's only Biscuit Co.
"This is going to be a test store," he says. "My goal is to obviously own several of them."
Carrie Rengers: 316-268-6340, @CarrieRengers
Testimonial at Mathnasium of West Wichita
QuikTrip tests carside service in Wichita
By Carrie Rengers
Ashby, we hardly knew ya. Ashby St. Outdoor, an Arkansas-based billboard company that entered the Wichita market in 2016, has sold to Baton Rouge-based Lamar Advertising for an undisclosed price.
MORE CARRIE RENGERS
Food isn't the focus of this new Artisan business
A different kind of food offering is opening where Rowdy Beaver, Willie C's were
Greenwich Place lands major outdoor retailer
New owner plans changes to drive traffic at Towne West Square
National companies tried to buy the Sprint campus, so how did a Wichita firm land it?


text:
Nearly in sniffing distance of a Christmas forecast this week but fortunately just 3 days too short to have our first guess at what Christmas Day will be like. For most of the south of the U.K (and Ireland I think) you'll be hoping for a dry week after Saturday's slow-moving rain front brought a lot more rain than forecast with some areas receiving over 30mm 🙁
The weather picture is really complicated at present and that makes it tricky to forecast with last weeks mild air now departed but soon to be joined by a fresh peak in the jet stream pushing more mild air across the U.K and Ireland. (Shown above on this Meteoblue schematic for Tuesday this week)
So for Monday we have a slight lull in the mild proceedings with a widespread ground frost for many except the south west of England where a rain front is set to push in first thing this morning. The other end of that front will also bring rain to the east coast of Munster. By mid-morning that front is pushing up into the south Midlands and is joined by another front over Kerry and the west coast of Ireland. Through the course of Monday this rain pushes up into Wales, the north west and it will affect most areas by the afternoon reaching the north and east last of course. The same for the Irish rain, it'll push across country during the day and into the north west of Scotland by evening. After a cold start, temperatures will rise as we go through the day and into the night because that rain is pushed along on a mild air front. So southerly winds and double figures for the south and west as the rain arrives and a mild, muggy night I'm afraid for many.
Tuesday sees that mild air very firmly in place and again we will see that familiar rain pattern crossing the south west of England during the morning and pushing across to the east and North Midlands by the early afternoon. For Ireland we have a dry start but rain will push into Kerry by lunchtime and move north and east into Munster, Leinster and Connacht through the afternoon. By dusk we will see that rain fairly well dispersed over most of the U.K but since it is a southerly front, Scotland may miss most of it for a largely dry day. Again unfeasibly mild with low to mid-teens in the south of England pushed up by that moderate southerly wind. Temperatures slightly lower in the north and Scotland where the mild air is having least affect.
For Wednesday we have more rain around to start the day wet but this time it'll be affecting east Munster and Leinster, the north west of England and south west / central regions of Scotland. South and east of this you look to be reasonably dry on Wednesday. That rain at this stage also looks to miss most of Wales just clipping the edge of Pembrokeshire and North Wales to boot during the morning. As we progress through the morning the rain will stay in situ over Scotland and the north west of England, West Wales and the south west tip of England, forming into a tight band by the evening. So a drier day for central and eastern areas and for most of Ireland as well. Again mild with a moderate southerly wind pushing temperatures into the low double figures across central and southern areas and a degree or two cooler across Scotland and Ireland.
For Thursday we see that band of rain drifting south overnight to push thick cloud into central and southern areas of the U.K. The rain will be in a line from Devon, up through Wales and pretty much following the M5 / M6 into northern England. Scotland and Ireland look to have a dry start to Thursday. It'll be only a temporary one I'm afraid for Ireland though as more rain is set to reach the west by mid-morning and push east across country through the afternoon. The rain over the U.K should fizzle out so some places staying dry on Thursday, particularly across the north, east and south east. A truly wet day for Ireland as that rain will still be sititng over you by dusk on Thursday. Slightly cooler on Thursday despite the moderate southerly wind with temperatures in the high single figures maybe just breaking into double figures in the south of England.
Closing out the week we see that rain that affected Ireland on Thursday moving slowly eastwards overnight into Friday across the west of England and into central areas by dawn sitting in a thick line from the Isle of Wight right up to northern England. Ireland and Scotland look to start dry and sunny especially across the west. That dry and bright weather will follow the rain eastwards so areas will clear from the west with cooler, brighter weather following behind. By lunchtime though we see more rain push into the west of Ireland and here it'll be an afternoon of sunshine and showers. That rain may also reach the north west of England and west of Scotland by the evening falling as wintry showers over higher ground. A change in the wind direction on Friday to north westerly and then westerly means a cooler day for many with high-single figures in the winter sunshine.
So onto the last weekend before Christmas and scenes of desperate shopping, queues, raised tempers and fuddled heads. Must try and get some fishing and cycling in.
Saturday looks a day of two halves, wet for Ireland from the off I'm afraid and some rain across the west of England and Wales as well. During the morning this will drift eastwards and fizzle out as it does so, so some areas staying dry throughout. The afternoon on Saturday looks dry for England and Wales, but that Irish rain front will push into the west of Scotland and this will move eastwards through the course of the afternoon to bring a wet end to the day there. A coolish day on Saturday with temperatures in the high single figures pushed along by a brisk south westerly wind. Sunday looks a similarly unsettled start to the day with brisk winds and some showers rattling in across the west and north initially but these will soon fizzle out as high pressure establishes itself to leave a dry, bright and cold day on Sunday with temperatures a degree or two down on Saturday.
So this one will take us nearly up to Christmas Day and over The Winter Solstice.
So Monday next week sees us continue that cooler feel to the weather with high pressure doing its best to push in from the east. So I think a cool, dry and bright start to the week, next week. During the mid part of the week we see a low pressure system push that high pressure south introducing milder and unsettled weather into Scotland and the north of Ireland from mid-week onwards. That high looks to cling on down south so I think a cool, dry and settled week for central regions of the U.K with night frosts and little rainfall. By the end of that week we may see that low push down from the north and where it encounters cooler air we may, just may see the odd snowflake. Maybe my Paddy Power White Christmas bets will come in for Edinburgh, Glasgow and Belfast, ahahaha………???? Of course a lot can and probably will change between now and then and most likely we'll all be walking around in shorts and T-Shirts come the day but as it stands now I'm not so sure on that 🙂
Last week I missed out our regular GDD bit for the start of a new month so let's kick off with that.
Wow just look at that, a total of 28 days of GDD in November making it the lowest November total we've recorded since (and including) 2010. The contrast between the very mild autumn / winter of 2015 and the cooler one of 2016 is clear to see with only 21% of the growth potential in November 2016 vs. November 2015. Is that a signal for a colder winter in January and February I wonder, we will have to wait and see..
Looking at the different locations we can see that the cold November theme was present across the U.K and Ireland with even Kerry substantially cooler than normal.
Growth and Disease Activity
As predicted at the beginning of last week we saw a mild temperature spike that gave us daytime air temperatures up to 17°C in the south of England and night time air temperatures that never dipped below 10°C as you can see above. In line with this prediction from a temperature perspective was the predicted increase in disease activity and this duly came to pass as well with Microdochium nivale, Red Thread, Superficial Fairy Ring and some Spiral Nematode activity noted at the back end of last week.
The combination of humidity and temperature was such that mycelium were visible on fine and coarse turf areas.
My feedback though was that we didn't see new Microdochium nivale infections on fine turf rather it was renewed activity on the edge of already-present scars. Again this was in line with predictions.
The pleasing aspect for me is that we are starting to get a good handle on not only a prediction of likely weather but more importantly you might argue, the potential effects of that weather on turf in terms of growth and disease activity. Now I fully appreciate rainfall remains a very variable weather parameter, both in terms of when it is likely to occur but also the level of the rainfall in terms of how much is likely to fall. Forecasted temperature however is I think a more reliable weather parameter and although there is variability per location, the resultant growth potential / GDD intepretation is quite accurate.
This will become more and more important in the future because predicting periods of disease activity and applying products preventatively before the disease outbreak will become the norm, especially as we lose more contact / curative chemistries (like Iprodione for example) going forward.
Looking ahead to this week we see another spike in Growth Potential on Tuesday before it drops back as we pick up cooler air later in the week so we may see another repeat of last week's disease activity though I think it'll be less severe in intensity.
Depending on where you are located you may have another opportunity to input some winter nutrition if the wind and rain plays ball. Certainly if you've applied a winter granular last week you should see a good response going through this week particularly where a high Fe product may have been used. Keeping the plant healthy through this part of the winter pays dividends when we come out the other side in late February, early March (if that's indeed when it is)
Once we get back in The New Year I'll be paying particularly attention to January / February aeration slots if they present themselves and it is nutrition applied during December that will help then in pushing nice and gentle recovery from aeration carried out in The New Year.
Ok that's it for now, next week's blog will cover the Christmas and New Year period and then I'll be having a well-earned week off 🙂
hi mark hope all is well
As regards fusarium patch activity in the south west of ireland particulary cork and kerry this has been a testing last 3 months. disease pressure and activity seems to be at a all time high with most courses having significant disease especially on higher high of cut areas and on greens most greenkeepers are finding it very difficult to keep it under control. a high percentage of courses in the area has high levels of scarring even after applying 4 or 5 fungicides since start of september with different active ingredients and modes of action no matter what u spray it does not seem to achieve sufficient control. i have never seen it so bad at our course with some fairways absolutely covered in it.
You paint a gloomy picture and one I recognise over here although I think here we are in better shape primarily because we have some other A.I's you don't (although we are losing them) and our weather is slightly less conducive to disease (we tend to be colder and drier than you as you can see from the GDD data). Microdochium started early this year with aggressive activity in the late summer / early autumn and once established it has proved difficult to shift. Key as always is preventing it establishing in the first place and the issue there is effective A.I's. At present the most effective systemic A.I's are Propiconazole and Tebuconazole and from our field resistance screening I've seen no indication of resistance in the field to their mode of action. The Strobilurins are showing poor efficacy with widespread resistance and Iprodione is showing a shift towards resistance so our options are limited. Over here we have been fortunate to be able to use Prochloraz and Tebuconazole in a combination approach and it has been very successful as a systemic, but with this going we will all have to work hard to keep turf healthy over the winter months. There are new A.I's and combinations of A.I's under development and hopefully we should see these start to filter through next year so relief is maybe in sight. Outfield areas this autumn have shown substantial Microdochium and Cortisium activity but again I think we should have something in the pipeline to help here at some point in the future.
Pete Gross
I feel sorry for those areas that had over 30m of rain 😉
Oh bugger I sense a typo error type feeling coming over me 🙁
Wordpress has no spell check so it's up to my alertness on a Monday morning Pete to correct errors.
I'm so happy you pointed this out to me 🙂
Hang on, my copy says "30mm" so I'm exonerated and I was clearly awake when I typed it !! 🙂


text:
Growth Plans: Is Tech's Stock Market Run Out of Gas? A Fintech Player Lifts Small Biz in India, Founders Scale for Opportunity, March Forth +More
As the world struggles to recover from a pandemic, the challenges and opportunities look different based on industry, geography, wealth — all kinds of factors. As I talk to entrepreneurs and investors working across so many of these factors, mobile and cloud technologies have the potential to make a significant difference in how those varied recovery efforts pan out. That's why the theme for this week is Growth Plans:
Is the Growth Trade Out of Gas? On the Other Hand
Look at Snowflake, DoorDash, Zoom: Is the growth trade out of gas? That was the question for my latest installment of On the Other Hand, on CNBC's Squawk Box:
Growing Small Business in India Through Fintech: Hardika Shah, Kinara Capital CEO
Hardika Shah founded Kinara Capital to help small businesses in India access capital to grow their businesses. Her own story is rich with calculated risk — including coming to the U.S. for college and staying the course during a pandemic:
Personal Growth: Forethought CEO Reflects on Entering Silicon Valley
Forethought Co-founder and CEO Deon Nicholas grew up in the housing projects of Toronto, in love with video games, basketball and programming. He didn't see many Black programming role models, so had to figure things out as he went. A pivotal moment: the internship offer from Facebook that brought him to Silicon Valley. We talked about his journey and Forethought's approach to artificial intelligence in a Fortt Knox conversation this week:
Cloud Growth Continues: Snowflake CEO on Demand for Flexibility
Snowflake CEO Frank Slootman joined me on CNBC after the company's earnings report to talk about what continues to fuel the company's momentum:
Boardroom Growth: Outreach Adds Depth and Diversity to Director Ranks
You might remember Outreach Co-founder and CEO Manny Medina from Fortt Knox about a year ago. He's got a software startup in Seattle that's working to make the sales process more efficient and effective. He also joined me in a CNBC special in December that focused on opportunity in America. Through that special, Manny met a longtime connection of mine, former AT&T Vice Chairman Ralph de la Vega. They came on CNBC this week to announce that Ralph has joined the Outreach board:
March Forth on March 4: A Conversation About Growing Social Impact
I've decided to try an occasional series on Fortt Knox that pulls back the curtain a bit. My friend, social impact leader Jayson Council, joined me to talk about how we're navigating family and work in a shifting society. Also joining us for this first chat: My friend Lloyd Pierce, recently coach of the NBA's Atlanta Hawks, who has had a major social impact himself:
Many readers of this newsletter will be familiar by now with my latest project: The Black Experience in America: The Course. I designed it last year after the tragic killing of George Floyd, and I'm building out an online learning experience based on the content.
The students of my alma mater, DePauw University, gained access to the course material this week. I'm looking forward to more collaborations that lead us to a deeper understanding of this moment in history, the ideas that got us here and the work to move forward.
If you're interested in exploring the same material, a bundle of three lessons is available for purchase here for $12.


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text:
Q: Why does the required attribute not work for input in HTML? I am a beginner so I am not aware of any conflicts that my code creates, but what I am trying to do is the following:
If the right option is selected from the dropdown, then an input field should appear and what I cannot seem to work out is to make input mandatory. I have tried adding the required attribute, but people could skip to the next part of the survey without filling in the input box. Adding the attribute class="obligatory" to the input does not help either because if another option is chosen, then the input does not show up and users cannot move on to the next part of the survey because the input field is hidden and obligatory at the same time.
I would be grateful for your help!

6. Mit welcher Sprache bist Du zuhause aufgewachsen?

<select id="selectBox" onchange="changeFunc();">
 <option value ="Bitte">Bitte eine Option auswählen</option>
 <option value="Dialekt">Dialekt</option>
 <option value="Umgangssprache">Umgangssprache</option>
 <option value="Gepflegtes Hochdeutsch">Gepflegtes Hochdeutsch</option>
 <option value="Beides">Beides</option>
</select>

<input name="dd_number" placeholder="Bitte Dialekt angeben" required minlength="1" maxlength="40" class="form-control" type="text" style="display: none" id="textboxes">

A: You can add the required attribute with jQuery only when your if clause applies: (and remove it from the HTML code before)
Note: You should also use removeAttr('reqired') on the else clause since if at first the "Dialekt" option is chosen, the attribute is added, but remains there if the selection is changed afterwards. That way it will be removed again.

6. Mit welcher Sprache bist Du zuhause aufgewachsen?

<select id="selectBox" onchange="changeFunc();">
 <option value ="Bitte">Bitte eine Option auswählen</option>
 <option value="Dialekt">Dialekt</option>
 <option value="Umgangssprache">Umgangssprache</option>
 <option value="Gepflegtes Hochdeutsch">Gepflegtes Hochdeutsch</option>
 <option value="Beides">Beides</option>
</select>

<input name="dd_number" placeholder="Bitte Dialekt angeben" minlength="1" maxlength="40" class="form-control" type="text" style="display: none" id="textboxes">

A: Since the form field already exists, you don't want to mess with attributes anymore, you are trying to change a "property" of the field.  To avoid confusion, jQuery now uses .prop() to make such changes.  Make sure to change it back to false when the user chooses another option!
See this fiddle:
https://jsfiddle.net/sv6phejL/

<form>

6. Mit welcher Sprache bist Du zuhause aufgewachsen?

<select id="selectBox" onchange="changeFunc();">
 <option value ="Bitte">Bitte eine Option auswählen</option>
 <option value="Dialekt">Dialekt</option>
 <option value="Umgangssprache">Umgangssprache</option>
 <option value="Gepflegtes Hochdeutsch">Gepflegtes Hochdeutsch</option>
 <option value="Beides">Beides</option>
</select>

<input name="dd_number" placeholder="Bitte Dialekt angeben" minlength="1" maxlength="40" class="form-control" type="text" style="display: none" id="textboxes">

<input type="submit">
</form>

A: This is not the answer but the complete chunk of code. I tried implementing all suggestions but I think that I might be unaware of subtleties here...
I would be immensely grateful for your help. The issue now is that there are warning pop-up boxes for each quesiton, which was not the case before...

      Information

Bevor wir mit dem Experiment anfangen, brauchen wir noch einige Informationen von Dir. Alle personenbezogenen Angaben werden anonymisiert gespeichert und eine spätere Zuordnung der angegebenen Daten zu Versuchspersonen wird den Forschenden nicht mehr möglich sein.
  Wenn Du fertig bist, klicke bitte auf "Fortsetzen".

1. Alter:<input name="Alter" type="text" size="30" class="obligatory"/>

2. Geschlecht:<input name="Geschlecht" type="radio" value="male" class="obligatory" id="cgendermale" />Männlich
                   <input name="Geschlecht" type="radio" value="female" id="cgenderfemale"/> Weiblich
                   <input name="Geschlecht" type="radio" value="divers" id="cgenderother"/> Divers

3. Was ist Dein höchster Bildungsabschluss?:
  <select  name="Bildungsabschluss" required="true" id="Bildungsabschluss">
      <option value="kein Abschluss">kein Abschluss</option>
      <option value="Schulabschluss">Schulabschluss</option>
      <option value="Abitur oder gleichwertiger Abschluss">Abitur oder gleichwertiger Abschluss</option>
      <option value="Studium ohne Abschluss">Studium ohne Abschluss</option>
    <option value="Ausbildung">Ausbildung</option>
    <option value="Bachelor">Bachelor</option>
    <option value="Master">Master</option>
    <option value="Promotion">Promotion</option>
 </select>

4. Stelle Dir vor, die Leiter (s. unten) repräsentiert den relativen Sozialstatus der Menschen in Deutschland. An der Spitze der Leiter stehen Menschen mit relativ hohem Status – diejenigen, die das meiste Geld, die beste Bildung und die angesehensten Arbeitsplätze haben. Ganz unten sind Menschen mit relativ niedrigem Status – beispielsweise als arbeitslos Gemeldete. Relativ weit unten zu verorten wären auch diejenigen, die nur wenig Geld verdienen, einen niedrigen Bildungstand haben, und / oder Berufe ausüben, die die Gesellschaft als eher wenig respektabel ansieht.

Wo würdest Du dich auf dieser Leiter einordnen? Wähle bitte die Sprosse, die Deinem empfundenen Sozialstatus am ehesten entspricht.

Deine Antwort:
 <select  name="Socialstatus" required="true" id="Socialstatus">
      <option value="10">A</option>
      <option value="9">B</option>
      <option value="8">C</option>
      <option value="7">D</option>
    <option value="6">E</option>
      <option value="5">F</option>
        <option value="4">G</option>
        <option value="3">H</option>
        <option value="2">I</option>
        <option value="1">J</option>
      </select>

5. Bist Du in Deutschland geboren?

  <input name="Herkunft" type="radio" value="Ja" class="obligatory" id="Herkunft_Ja" />Ja
                   <input name="Herkunft" type="radio" value="Nein" id="Herkunft_Nein"/> Nein

6. Mit welcher Sprache bist Du zuhause aufgewachsen?

<select id="selectBox" onchange="changeFunc();">
 <option value ="Bitte">Bitte eine Option auswählen</option>
 <option value="Dialekt">Dialekt</option>
 <option value="Umgangssprache">Umgangssprache</option>
 <option value="Gepflegtes Hochdeutsch">Gepflegtes Hochdeutsch</option>
 <option value="Beides">Beides</option>
</select>

<input name="dd_number" placeholder="Bitte Dialekt angeben" class="form-control" type="text" style="display: none" id="textboxes">

7. Welche Sprache sprichst Du aktuell hauptsächlich?

<select id="selectB" onchange="changeFunction();">
<option value ="Bitte">Bitte eine Option auswählen</option>
<option value="Dialekt">Dialekt</option>
<option value="Umgangssprache">Umgangssprache</option>
<option value="Gepflegtes Hochdeutsch">Gepflegtes Hochdeutsch</option>
<option value="Beides">Beides</option>
</select>
<input name="d_number" placeholder="Bitte Dialekt angeben" class="form-control" type="text" style="display: none" id="textboxes2">

8. Wie gut (Skala von 0 - "gar nicht" bis 100 "ausgezeichnet") sprichst und verstehst Du:

<form action="/action_page.php">
  a. Dialekt:

  <input type="text" id="Dialekt_skala" name="Skala" type="text" size="30" class="obligatory"/>

  b. Hochsprache:

  <input type="text" id="Hochsprache_skala" name="Skala" type="text" size="30" class="obligatory"/>

redpajama_set_name: RedPajamaStackExchange


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#2 could read that way because I installed the latest firmware.
Does anyone have any ideas on how I can get this working? I specifically ordered the WG511 last week on Amazon so I wouldn't have to use ndiswrapper.
sorry, but you'll have to either get a new wg511 that's made in taiwan, try another wifi card (i've had decent luck with my dlink dwl 122 802.11b as well), or just use ndiswrapper/linuxant driverloader.


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