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Despite the recent success of deep neural networks in natural language processing (NLP), their interpretability remains a challenge.
The missing ingredient in zero-shot neural machine translation
Arivazhagan, Naveen, Ankur Bapna, Orhan Firat, Roee Aharoni, Melvin Johnson, and Wolfgang Macherey. 2019 · 1903
Earlier work this paper cites.
To tune or not to tune? adapting pretrained representations to diverse tasks
Peters, Matthew, Sebastian Ruder, and Noah A Smith. 2019 · 1903
Earlier work this paper cites.
Translation
Weaver, Warren. 1955 · 1955
Earlier work this paper cites.
Dependency Syntax: Theory and Practice
Mel’čuk, Igor Aleksandrovič. 1988 · 1988
Earlier work this paper cites.
Using Test Suites in Evaluation of Machine Translation Systems
King, Margaret and Kirsten Falkedal. 1990 · 1990
Earlier work this paper cites.
Distributed representations, simple recurrent networks, and grammatical structure
Elman, Jeffrey L. 1991 · 1991
Earlier work this paper cites.
Part-of-Speech Tagging with Neural Networks
Schmid, Helmut. 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Hochreiter, Sepp and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Supertagging: An approach to almost parsing
Bangalore, Srinivas and Aravind K. Joshi. 1999 · 1999
Earlier work this paper cites.
BLEU: a Method for Automatic Evaluation of Machine Translation
Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
A Decoder for Syntax-based Statistical MT
Yamada, Kenji and Kevin Knight. 2002 · 2002
Earlier work this paper cites.
Empirical Methods for Compound Splitting
Koehn, Philipp and Kevin Knight. 2003 · 2003
Earlier work this paper cites.
Statistical phrase-based translation
Koehn, Philipp, Franz Josef Och, and Daniel Marcu. 2003 · 2003
Earlier work this paper cites.
Annotation of English on the tectogrammatical level: Reference book
Cinková, Silvie, Jan Hajič, Marie Mikulová, Lucie Mladová, Anja Nedolužko, Petr Pajas, Jarmila Panevová, Jiří Semeckỳ, Jana Šindlerová, Josef Toman, Zdeňka Urešová, and Zdeněk Žabokrtský. 2004 · 2004
Earlier work this paper cites.
The Importance of Supertagging for Wide-Coverage CCG Parsing
Clark, Stephen and James R. Curran. 2004 · 2004
Earlier work this paper cites.
A Hierarchical Phrase-based Model for Statistical Machine Translation
Chiang, David. 2005 · 2005
Earlier work this paper cites.
Dependency Grammar and Dependency Parsing
Nivre, Joakim. 2005 · 2005
Earlier work this paper cites.
Scalable Inference and Training of Context-Rich Syntactic Translation Models
Galley, Michel, Jonathan Graehl, Kevin Knight, Daniel Marcu, Steve DeNeefe, Wei Wang, and Ignacio Thayer. 2006 · 2006
Earlier work this paper cites.
Creating a CCGbank and a wide-coverage CCG lexicon for German
Hockenmaier, Julia. 2006 · 2006
Earlier work this paper cites.
Phrase Reordering for Statistical Machine Translation Based on Predicate-Argument Structure
Komachi, Mamoru, Yuji Matsumoto, and Masaaki Nagata. 2006 · 2006
Earlier work this paper cites.
Multilingual Dependency Analysis with a Two-Stage Discriminative Parser
McDonald, Ryan, Kevin Lerman, and Fernando Pereira. 2006 · 2006
Earlier work this paper cites.
User’s guide to sigf
Padó, Sebastian. 2006 · 2006
Earlier work this paper cites.
CCGbank: a corpus of CCG derivations and dependency structures extracted from the Penn Treebank
Hockenmaier, Julia and Mark Steedman. 2007 · 2007
Earlier work this paper cites.
Factored translation models
Koehn, Philipp and Hieu Hoang. 2007 · 2007
Earlier work this paper cites.
Moses: Open source toolkit for statistical machine translation
Koehn, Philipp, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al. 2007 · 2007
Earlier work this paper cites.
A Tree-to-Tree Alignment-based Model for Statistical Machine Translation
Zhang, Min, Hongfei Jiang, AiTi Aw, Jun Sun, Sheng Li, and Chew Lim Tan. 2007 · 2007
Earlier work this paper cites.
A Simple and Effective Hierarchical Phrase Reordering Model
Galley, Michel and Christopher D. Manning. 2008 · 2008
Earlier work this paper cites.
11,001 New Features for Statistical Machine Translation
Chiang, David, Kevin Knight, and Wei Wang. 2009 · 2009
Earlier work this paper cites.
Tectogrammatical Annotation of the Wall Street Journal
Cinková, Silvie, Josef Toman, Jan Hajič, Kristýna Čermáková, Václav Klimeš, Lucie Mladová, Jana Šindlerová, Kristýna Tomšů, and Zdeněk Žabokrtský. 2009 · 2009
Earlier work this paper cites.
Hindi-to-Urdu Machine Translation through Transliteration
Durrani, Nadir, Hassan Sajjad, Alexander Fraser, and Helmut Schmid. 2010 · 2010
Earlier work this paper cites.
A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages
Luong, Minh-Thang, Preslav Nakov, and Min-Yen Kan. 2010 · 2010
Earlier work this paper cites.
String-to-Dependency Statistical Machine Translation
Shen, Libin, Jinxi Xu, and Ralph Weischedel. 2010 · 2010
Earlier work this paper cites.
KenLM: Faster and smaller language model queries
Heafield, Kenneth. 2011 · 2011
Earlier work this paper cites.
Analyzing and Integrating Dependency Parsers
McDonald, Ryan and Joakim Nivre. 2011 · 2011
Earlier work this paper cites.
Combinatory Categorial Grammar , chapter 5. John Wiley and Sons, Ltd
Steedman, Mark and Jason Baldridge. 2011 · 2011
Earlier work this paper cites.
Extracting Pre-ordering Rules from Predicate-Argument Structures
Wu, Xianchao, Katsuhito Sudoh, Kevin Duh, Hajime Tsukada, and Masaaki Nagata. 2011 · 2011
Earlier work this paper cites.
Semantics-Based Machine Translation with Hyperedge Replacement Grammars
Jones, Bevan, Jacob Andreas, Daniel Bauer, Moritz Karl Hermann, and Kevin Knight. 2012 · 2012
Earlier work this paper cites.
Combining Word-Level and Character-Level Models for Machine Translation Between Closely-Related Languages
Nakov, Preslav and Jörg Tiedemann. 2012 · 2012
Earlier work this paper cites.
Modeling the Translation of Predicate-Argument Structure for SMT
Xiong, Deyi, Min Zhang, and Haizhou Li. 2012 · 2012
Earlier work this paper cites.
Modeling Syntactic and Semantic Structures in Hierarchical Phrase-based Translation
Li, Junhui, Philip Resnik, and Hal Daumé III. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Investigating the Usefulness of Generalized Word Representations in SMT
Durrani, Nadir, Philipp Koehn, Helmut Schmid, and Alexander Fraser. 2014a · 2014
Earlier work this paper cites.
Integrating an Unsupervised Transliteration Model into Statistical Machine Translation
Durrani, Nadir, Hassan Sajjad, Hieu Hoang, and Philipp Koehn. 2014b · 2014
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Adam: A Method for Stochastic Optimization
Kingma, Diederik and Jimmy Ba. 2014 · 2014
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On the Elements of an Accurate Tree-to-String Machine Translation System
Neubig, Graham and Kevin Duh. 2014 · 2014
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Rdrpostagger: A ripple down rules-based part-of-speech tagger
Nguyen, Dat Quoc, Dai Quoc Nguyen, Dang Duc Pham, and Son Bao Pham. 2014 · 2014
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SemEval 2014 Task 8: Broad-Coverage Semantic Dependency Parsing
Oepen, Stephan, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Dan Flickinger, Jan Hajic, Angelina Ivanova, and Yi Zhang. 2014 · 2014
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Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder
Dalvi, Fahim, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, and Stephan Vogel. 2017 · 2017
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Interpretation of Semantic Tweet Representations
Ganesh, J., Manish Gupta, and Vasudeva Varma. 2017 · 2017
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Convolutional Sequence to Sequence Learning
Gehring, Jonas, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017 · 2017
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Producing unseen morphological variants in statistical machine translation
Huck, Matthias, Aleš Tamchyna, Ondřej Bojar, and Alexander Fraser. 2017 · 2017
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Hupkes, Dieuwke, Sara Veldhoen, and Willem Zuidema. 2017 · 2017
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MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic
Pasha, Arfath, Mohamed Al-Badrashiny, Mona Diab, Ahmed El Kholy, Ramy Eskander, Nizar Habash, Manoj Pooleery, Owen Rambow, and Ryan Roth. 2014 · 2014
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Morfessor 2.0: Toolkit for statistical morphological segmentation
Smit, Peter, Sami Virpioja, Stig-Arne Grönroos, and Mikko Kurimo. 2014 · 2014
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Sequence to Sequence Learning with Neural Networks
Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
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The operation sequence Model—Combining n-gram-based and phrase-based statistical machine translation
Durrani, Nadir, Helmut Schmid, Alexander Fraser, Philipp Koehn, and Hinrich Schütze. 2015 · 2015
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Visualizing and Understanding Recurrent Networks
Karpathy, Andrej, Justin Johnson, and Fei-Fei Li. 2015 · 2015
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Character-aware Neural Language Models
Kim, Yoon, Yacine Jernite, David Sontag, and Alexander M Rush. 2015 · 2015
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Character-based neural machine translation
Ling, Wang, Isabel Trancoso, Chris Dyer, and Alan W. Black. 2015 · 2015
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Google’s multilingual neural machine translation system: Enabling zero-shot translation
Johnson, Melvin, Mike Schuster, Quoc Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernand a Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2017 · 2017
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Representation of linguistic form and function in recurrent neural networks
Kádár, Akos, Grzegorz Chrupała, and Afra Alishahi. 2017 · 2017
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Fully character-level neural machine translation without explicit segmentation
Lee, Jason, Kyunghyun Cho, and Thomas Hofmann. 2017 · 2017
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Learned in translation: Contextualized word vectors
McCann, Bryan, James Bradbury, Caiming Xiong, and Richard Socher. 2017 · 2017
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Predicting Target Language CCG Supertags Improves Neural Machine Translation
Nadejde, Maria, Siva Reddy, Rico Sennrich, Tomasz Dwojak, Marcin Junczys-Dowmunt, Philipp Koehn, and Alexandra Birch. 2017 · 2017
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Universal Dependencies 2.0
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Tilde’s machine translation systems for wmt 2017
Pinnis, Mārcis, Rihards Krišlauks, Toms Miks, Daiga Deksne, and Valters Šics. 2017 · 2017
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Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings
Rios Gonzales, Annette, Laura Mascarell, and Rico Sennrich. 2017 · 2017
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How grammatical is character-level neural machine translation? assessing mt quality with contrastive translation pairs
Sennrich, Rico. 2017 · 2017
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The university of edinburgh’s neural mt systems for wmt17
Sennrich, Rico, Alexandra Birch, Anna Currey, Ulrich Germann, Barry Haddow, Kenneth Heafield, Antonio Valerio Miceli Barone, and Philip Williams. 2017 · 2017
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A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions
Toral, Antonio and Víctor M. Sánchez-Cartagena. 2017 · 2017
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Attention is All you Need
Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Gate Activation Signal Analysis for Gated Recurrent Neural Networks and Its Correlation with Phoneme Boundaries
Wang, Yu-Hsuan, Cheng-Tao Chung, and Hung-yi Lee. 2017 · 2017
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Sequence-to-Dependency Neural Machine Translation
Wu, Shuangzhi, Dongdong Zhang, Nan Yang, Mu Li, and Ming Zhou. 2017 · 2017
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Evaluating discourse phenomena in neural machine translation
Bawden, Rachel, Rico Sennrich, Alexandra Birch, and Barry Haddow. 2018 · 2018
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On Internal Language Representations in Deep Learning: An Analysis of Machine Translation and Speech Recognition
Belinkov, Yonatan. 2018 · 2018
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The lazy encoder: A fine-grained analysis of the role of morphology in neural machine translation
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BPE and CharCNNs for Translation of Morphology: A Cross-Lingual Comparison and Analysis
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