multilingual neural machine translation with language clusteringmexican restaurant wiesbaden

His research interests cover machine learning, deep learning, and their applications on natural language/speech/music processing, including neural machine translation, pre-training, neural architecture search, text to speech, automatic speech recognition, music understanding and generation, etc. compare representations across different languages, layers and models. Chen Zhang, Yi Ren, Xu Tan, Jinglin Liu, Kejun Zhang, Tao Qin, Sheng Zhao, Tie-Yan Liu. This material is based upon work supported in part by the Defense Advanced Research Projects Agency Information Innovation Office (I2O) Low Resource Languages for Emergent Incidents (LORELEI) program under Contract No. \includegraphics[width=.9\linewidth]{pics/koen_enc_ft.png} Specific Attention Heads? The performance of sub-sep, on the other hand, decreases by around 1.5 BLEU when training on all languages for bel. Spanish & 1 & Catalan $\star$ & Catalan $\star$ \\ 02/28/2019 ∙ by Roee Aharoni, et al.

If you want to know about climate, you ask a redclimatologist. Comment. Figure 3 shows the percentage of the word pairs grouped by their edit distance. share, Transfer learning or multilingual model is essential for low-resource ne... But we don’t have a technology to solve that, redright? representations (with 103 languages) using Singular Value Canonical Correlation Found inside – Page 9Potential future work includes investigating alternative high-quality semantic similarity scores, filtering high quality bitext corpora for machine translation, or embedding quality measure into end-to-end language generation models.

Abstract. Jiatao Gu, Hany Hassan, Jacob Devlin, and Victor O. K. Li. (Stanford NLP Group, last access June 02, 2020).

\label{fig:baselines:en_x} ∙ As demonstrated in Figure 2, given a word w in a multilingual corpus from language Li, SDE constructs the embedding of w in three phases. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. For the baseline, we use the standard lookup embeddings for three granularities of lexical units: (1) word: with a fixed word vocabulary size of 64,000 for the concatenated bilingual data; (2) sub-joint: with BPE of 64,000 merge operations on the concatenated bilingual data; and (3) sub-sep: with BPE separately on both languages, each with 32,000 merge operations, effectively creating a vocabulary of size 64,000. Xu Tan, Yichong Leng, Jiale Chen, Yi Ren, Tao Qin, Tie-Yan Liu. John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. ∙ Towards this end, we perform a comprehensive study on massively multilingual neural machine translation tasks, where each language pair is considered as a separate task. % Similarly, languages written in the Arabic script share not only the superficial property of a common character set, but also (usually) an association with Islam, through the historical influence of the Ottoman and Moghul empires. Ye Qi, Devendra Singh Sachan, Matthieu Felix, Sarguna Padmanabhan, and Graham Multilingual training of neural machine translation (NMT) systems has led to Lijun Wu, Xu Tan, Tao Qin, Jianhuang Lai, Tie-Yan Liu. % Two such factors that may be especially influential on the domain of our training data are politics and religion. Resource Size & Languages \\\hline Diversity: Our corpus has languages belonging to a wide variety of scripts and linguistic families. These two conflicting objectives are difficult to achieve through existing methods. We are rolling an update to our APIs that allows analyzing texts in fifty-seven languages. ∙ Corsican & co & Irish & ga & Persian & fa & Urdu & ur \\ \item Uses Canonical Correlation Analysis (CCA) \cite{hardoon2004canonical} to linearly transform $l’_1$ and $l’_2$ to be as aligned as possible, i.e., CCA computes $\tilde{l_1} = W_1 l’_1$ and $\tilde{l_2} = W_2 l’_2$ to maximize the correlations $\bar{\rho} = \{ \rho_1, ..., \rho_{min(d’_1, d’_2)} \}$ between the new subspaces. Macduff Hughes, and Jeffrey Dean. The first step to compute the neural representation for a sentence is to segment the sentence into lexical units. Rapid adaptation of neural machine translation to new languages. Bridging Linguistic Typology and Multilingual Machine Translation . If you want to know about climate, you’re asking a redcollege friend. \includegraphics[scale=0.225]{baselines_en_to_any.png} \caption{Trendlines depicting translation performance of the massively multilingual model (blue curves) compared to bilingual baselines (solid black lines). Analysis (SVCCA), a representation similarity framework that allows us to The effect of the language specific transformation is smaller and is language dependent. 2019. A Substack newsletter by Bugra Akyildiz. \caption{Embeddings, colored by script} However, it is also possible to have multiple versions of embeddings for a single lexicon and combine them through operations such as attentional weighted sum (Gu et al., 2018). We can see that SDE is better at capturing functional words like “if” and “would”. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing. The y-axis depicts the number of training examples available per language pair on a logarithmic scale. \hline About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . We would also like to thank Maithra Raghu for multiple insightful discussions about our methodology. ∙ \end{subfigure} This problem is especially salient when the high-resource language dominates the training data (see empirical results in Section 4). Figure 6 shows that the lowest KL divergence is generally on the diagonals representing words with identical meanings, which indicates that similar words from two related languages tend to have similar attention over the latent embedding space. \includegraphics[width=.9\linewidth]{pics/ruen_enc_ft.png} Subword segmentation strikes a middle ground, but has many potential problems for multilingual NMT, as already discussed in Section 2.2. \end{subfigure} SDE shares such semantic representations among languages by querying a list of shared concepts, which are loosely related to the linguistic concept of “sememes” (Greimas, 1983). & 5 & \textit{Gujarati} $\star$ & \textit{Marathi} $\star$ \\ \small \subsection{SVCCA}\label{sub:svcca} EMNLP 2019. Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research. Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes. ber of languages used in electronic texts for inter-national communications has promoted Machine Translation (MT) systems to shift towards multi-lingualism. \caption{Comparison of X-En pairs with baselines.}

Figure \ref{fig:tree} provides a more detailed look into the Slavic languages, and how this compositionality maps to the established family tree for Slavic languages. Moreover, it eliminates unknown words without any external preprocessing step such as subword segmentation. \caption{The change in distribution of pairwise SVCCA scores between language pairs across layers of a multilingual NMT model, with SVCCA scores between English-to-Any and Any-to-English language pairs visualized separately. Mingjian Chen, Xu Tan, Yi Ren, Jin Xu, Hao Sun, Sheng Zhao, Tao Qin, Tie-Yan Liu. Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs. \begin{subfigure}{0.98\columnwidth} However, representations at the top of the encoder are far from perfectly aligned, possibly indicating that different languages are represented in only partially overlapping sub-spaces. These selected languages form a mix of high and low resource language pairs, from 6 language sub-families.\footnote{More details on the relative resource sizes of different language pairs can be found in the Appendix \ref{sub:ft-size}.} Note that all of these categories\footnote{When we refer to languages from a certain category, we only list those that are in our dataset. mult... These observations suggest that high resource languages might be responsible for partitioning the representation space, while low-resource languages become closely intertwined with linguistically similar high-resource languages. It furhter enables easy exploitation of pre-trained models using monolingual corpus . \begin{subfigure}{0.5\textwidth} As expected from its status as an isolate, the nearest neighbors in the embedding space are a nonsensical mix of languages. Fully character-level neural machine translation without explicit Clustering or cluster analysis is an unsupervised learning problem. \centering We also find in experiments in Section 4 that it is actually less robust than simple lookup when large monolingual data to pre-train embeddings is not available, which is the case for many low-resourced languages. \begin{subfigure}{0.5\textwidth} This is also in line with some findings of studies on translationese \cite{koppel2011translationese}, where the authors show that that the translated text is predictive of the source language. inspired by work in multilingual neural machine translation (NMT) (Tan et al.,2019), we investi-gate a method for grouping similar languages using an automated clustering method. However, by the top of the encoder the top four nearest neighbors are those languages geographically closest to Basque country (excepting French), probably reflecting lexical borrowing or areal influences on Basque. \begin{subfigure}{0.5\textwidth} The authors thank David Mortensen for helpful comments, and Amazon for providing GPU credits. arXiv e-prints, page arXiv:1909.07342. The approach is informed by our previous work on machine learning (Barzdins, Paikens, Gosko, 2013), media monitoring (Barzdins et al.,2014), and character-level neural translation (Barzdins & Gosko, 2016). We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair. \includegraphics[width=.9\linewidth]{pics/uken_enc_ft.png} It improve zero-shot translation. Lower-resource languages, however, tend to produce much noisier representations in the embeddings. 1. They have shown that clustering similar languages yields better translation results as compared to ran-domly clustered languages. Both the encoder and decoder show clustering according to linguistic similarity.} [ Paper ] Yingce Xia, Xu Tan, Fei Tian, Fei Gao, Weicong Chen, Yang Fan, Linyuan Gong, Yichong Leng, Renqian Luo, Yiren Wang, Lijun Wu, Jinhua Zhu, Tao Qin, Tie-Yan Liu, Microsoft Research Asia's . \caption{Visualization of the embedding layer for three branches of the Indo-European language family, coloring by different attributes to highlight clusters. ∙ We test SDE on four low-resource languages from a multilingual TED corpus (Qi et al., 2018). For the Any-to-English (X-En) language pairs (Figure \ref{fig:xen-var}), we notice that similarity between the source languages (X) increase as we move up the encoder, from embeddings towards higher level encoder layers, suggesting that the encoder attempts to learn a common representation for all source languages. \begin{figure*}[t!] share, Transferring representations from large supervised tasks to downstream t... SDE represents a word by its Additionally, training character-based NMT systems is often slow, due to the longer character sequences. In this subsection we plot the extent to which the representation space changes on average across language pairs (ie, decrease in SVCCA score) for different layers in Figure \ref{fig:ft-ld} on finetuning with these language pairs: ru-en (Russian), ko-en (Korean), uk-en (Ukrainian), km-en (Khmer). \begin{minipage}{0.5\textwidth} We find that the trends discussed above are generally true for other language groupings too. Subword-based segmentation is a middle ground between word and character segmentation. 0 \label{fig:slavoturkic-clusters:emb-fam} 08/25/2019 ∙ by Xu Tan, et al. Multilingual Neural Machine Translation (NMT) models have demonstrated great improvements for cross-lingual transfer, on tasks including low-resource language translation zoph2016transfer; nguyen-chiang:2017:I17-2; neubig2018rapid and zero or few-shot transfer learning for downstream tasks eriguchi2018zero; lample2019cross; DBLP:journals/corr . However, existing approaches suffer from performance degradation — a single multilingual model is inferior to separately trained bilingual . \centering \section{Discussion}\label{sect:impl} the target language, and vice-versa, and (iii) Representations of high resource Yan Zhao, Weicong Chen, Xu Tan, Kai Huang, Jin Xu, Changhu Wang, Jihong Zhu. In this paper, we empirically introduce a simple method to translate between . 58 languages in the paper) • Next fine-tune the model on a new low-resource language Model θ English English French Hindi … Turkish Model Belurasian θ English Initialize Rapid adaptation of Neural Machine Translation to New Languages. This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. During training, we use a temperature based data sampling strategy, similar to the strategy used to train the multilingual models in \cite{arivazhagan2019massively}. \centering We further inspect the improvements by calculating the word F-measure of the translated target words based on two properties of their corresponding source words: 1) the number of subwords they were split into; 2) the edit distance between their corresponding words in the related high-resource language. We examine the performance of SDE and the best baseline sub-sep, with a character n-gram vocabulary and sub-word vocabulary respectively, of size of 8K, 16K, and 32K. \includegraphics[width=0.8\linewidth]{pics/tok_svccaenxenc.png} That is, if $p_L$ is the probability that a sentence in the corpus belongs to language pair $L$, we sample from a distribution where the probability of sampling from $L$ is proportional to ${p_L}^{\frac{1}{T}}$. machine translation?

We present a model . Amir H. Jadidinejad Models trained on this massive open-domain dataset are expected to yield rich, complex representations which we attempt to study in this paper. \label{fig:slavoturkic-clusters} \label{fig:slavoturkic-clusters:enc5-resource} \label{fig:tok-evolve} Found inside – Page 315Google's multilingual neural machine translation system: enabling zero-shot translation. arXiv preprint arXiv:1611.04558 (2016) Lange, T., Roth, V., Braun, M.L., Buhmann, J.M.: Stability-based validation of clustering solutions. Neural ... \label{sec:supplemental} latent embedding space shared by all languages. Catalan & ca & Icelandic & is & Norwegian & no & Thai & th \\

We do this for all languages, in order to understand which factors determine the extent of distortion. % Unfortunately there is no one axis along which to measure similarity -- in fact there are several whole subfields of linguistics devoted to this question, including Comparative Linguistics and Linguistic Typology \cite{brittanicalanguageclassification}. Domain: Fine Tuning. ∙ \end{figure*} The volume is relevant not only to researchers in language endangerment, language shift and language death, but to anyone interested in the languages and cultures of the world. While we do see some amount of clustering according to linguistic similarity, the clusters are less separated than in Figure \ref{fig:top-clusters}. \subsection{Model and Training Details} From Figure 4 left, we can see that SDE is better at predicting words that were segmented into a large number of subwords. \includegraphics[width=.8\linewidth]{IndoIranianDravidian-enc5-fam.png} \begin{subfigure}{\textwidth} ∙ We furthermore demonstrate that within macro-clusters corresponding to languages of particular families, there exist micro-clusters corresponding to branches within those families. The example of Yiddish is given in Table \ref{nns}. E-mail: [email protected], Homepage: https://tan-xu.github.io/. For English-to-Any (En-X) language pairs (Figure \ref{fig:enx-var}) we observe a similar trend. We first attempt to quantify the extent of distortion in language representations caused by the fine-tuning process. We calculate the KL divergence of the attention distribution for word pairs in both the LRL and HRL. Our method, used without any subword segmentation, shows significant improvements over the strong multilingual NMT baseline on all languages tested. \subsubsection*{SVCCA Across Languages} Although the language family is important to these clusters, it is important to note the apparent role that writing system also plays in these visualizations. For vocabulary, we use a Sentence Piece Model, 2xx> token prepended to the source sentence to indicate the target language, as in \cite{johnson2017google}. 1980). We use the Transformer-Big vaswani2017attention architecture containing 375M parameters described in (chen-EtAl:2018:Long1; arivazhagan2019massively), for our experiments and share all parameters across language pairs including softmax layer and input/output word embeddings. An exception to these general trends seems to be fine-tuning on ny-en (Nyanja: Benue-Congo sub-family); all language pairs degrade by roughly the same extent, irrespective of language similarity or resource size. \includegraphics[width=.8\linewidth]{IndoIranianDravidian-emb-script.png} \centering low-r... Results are shared in Figure~\ref{fig:baselines}. Our analysis reveals that language representations cluster based on language similarity. Since they have no overlap in subword vocabulary, we conclude that they cluster purely based on distributional similarity -- even at the level of sub-word embeddings. \end{subfigure} \begin{subfigure}{0.5\textwidth} Finally, all models are optimized using Adafactor optimizer \citep{shazeer2018adafactor} with momentum factorization and a per-parameter norm clipping threshold of 1.0. \caption{List of BCP-47 language codes used throughout this paper \cite{bcp47}.}. Acknowledgements: \caption{Visualization depicting the (a) change in representations (using SVCCA) and (b) relative change in performance (in terms of test BLEU) of \textit{xx-en} language pairs (x-axis), after fine-tuning a large multilingual model on various X-En language pairs (y-axis). Multilingual Neural Machine Translation (NMT) models have yielded large empirical success in transfer learning settings. . This design is implemented using an attention mechanism, where the query is the lexical unit representation, and the keys and the values come from an embedding matrix shared among all languages. A similar example is Urdu, where the embeddings seem to be more influenced by less-related languages written in the same (Arabic) script, whereas by the top of the encoder, the neighbor list is a quite high-quality ranking of similar languages in entirely different scripts. Lawrence has taken the method of "transformation first, clustering second" to study Russian-English multi-language text clustering by translating the full text via machine translation systems and . Without loss of generality, we can assume there is always a transformation applied to the embedding vectors, and models that do not use such a transformation can be treated as using the identity transformation. BLEU scores presented in this paper are calculated on true-cased output and references, where we used mteval-v13a.pl script from Moses. While there are a few outliers, we can observe some overlapping clusters, including the Slavic cluster on the top-left, the Germanic and Romance clusters on the bottom-left, the Indo-Aryan and Dravidian clusters on the top-right, etc. Wu. \end{figure*} \label{fig:indo-iranian-dravidian-clusters:emb-script} In this section, we attempt to replicate some results in the paper using a token-level CCA strategy, and discuss the differences in our results. Basque & eu & Hausa & ha & Malayalam & ml & Sundanese & su \\ A further inspection of the four language pairs shows that the language specific transform is more helpful for training on languages with fewer words of the same spelling. This tendency becomes even stronger as we go up the encoder. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g. & 5 & \textit{Korean} & \textit{Macedonian} \\ Multilingual neural machine translation with language clustering X Tan, J Chen, D He, Y Xia, T Qin, TY Liu 2019 Conference on Empirical Methods in Natural Language Processing and the … , 2019 Let me read it first.

Multilingual NMT with a language-independent attention bridge, https://scikit-learn.org/stable/modules/generated/sklearn.manifold.SpectralEmbedding.html. analysis validates several empirical results and long-standing intuitions, and

Yuzi Yan, Xu Tan, Bohan Li, Tao Qin, Sheng Zhao, Yuan Shen, Tie-Yan Liu. This is perhaps the natural choice for lexical unit segmentation. He received his PhD in computational linguistics for work on bitext alignment and machine translation from Uppsala University before moving to the University of Groningen for 5 years of post-doctoral research on question answering and information extraction.

Bengali & bn & Hebrew & iw & Maori & mi & Swedish & sv \\
Linghui Meng, Jin Xu, Xu Tan, Jindong Wang, Tao Qin, Bo Xu. 08/25/2019 ∙ by Xu Tan, et al. Xu Tan at Microsoft Mingjian Chen, Xu Tan, Bohan Li, Yanqing Liu, Tao Qin, Sheng Zhao, Tie-Yan Liu. \end{subfigure}

Found inside – Page 247Sarker, A., Gonzalez, G.: Portable automatic text classification for adverse drug reaction detection via multi-corpus ... F.B.: A comparison of transformer and recurrent neural networks on multilingual neural machine translation, pp. Is the extent of representational overlap similar throughout the model? and compression. Given there are thousands of languages in the world and some of them are very different, it is extremely . Out-of-vocab n-grams are mapped to a designated token ⟨unk⟩. We also compare the distributions of pairwise SVCCA scores using our pooling strategy and a naive token-wise strategy between English-to-Any language pairs across layers of the encoder. \hline Yiddish & 1 & \textit{Lao} & \textit{German} $\star\star$ \\ & 2 & \textit{German} $\star\star$ & \textit{Norwegian} $\star$ \\ We see a similar pattern with models fine-tuned on es-en (Spanish), ca-en (Catalan) and the Romance languages, uk-en (Ukranian), sr-en (Serbian), ru-en (Russian) and the Slavic languages. Until now, to analyze texts in different languages, we needed to .

\begin{minipage}{0.5\textwidth} • First, do multilingual training on many languages (eg. In this section we compare the attention distribution over the latent embedding space of related languages, with the intuition that words that mean the same thing should have similar attention distributions. \centering \subsection{What is Language Similarity?} \end{figure*} the face of paucity of data.


Multilingual Neural Machine Translation with Language Clustering. For multilingual NMT settings, where multiple languages are processed, the number of words mapped to ⟨unk⟩ significantly increases. Multilingual Neural Machine Translation With Soft ... \subsubsection*{Low-resource, script-diverse language families} (SDE), a multilingual lexicon encoding framework specifically designed to share Yan Lu, Yuanchao Shu, Xu Tan, Yunxin Liu, Mengyu Zhou, Qi Chen, Dan Pei. MLOps Newsletter | Bugra Akyildiz | Substack We first visualize a clustering for all languages together in Figure \ref{fig:allcluster}. share. Facebook AI Research Lab, Paris, June 21 2019. Request PDF | A Study of Multilingual Neural Machine Translation | Multilingual neural machine translation (NMT) has recently been investigated from different aspects (e.g., pivot translation . This puts a large amount of pressure on neural models, requiring larger model sizes and training data. The Appendix shows an example with the Dravidian, Indo-Aryan, and Iranian language families, demonstrating the same phenomena discussed above (Appendix Figure~\ref{fig:indo-iranian-dravidian-clusters}). \hline \centering Found inside – Page 221Neural Comput. 9(8), 1735–1780 (1997) 9. Jabi, M., Pedersoli, M., Mitiche, A., Ayed, I.B.: Deep clustering: on the link ... transfer learning in multilingual neural machine translation with cross-lingual word embeddings (2021) 19. \includegraphics[width=.8\linewidth]{slavoturkic-enc5-fam.png}

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