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Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of language.
To tune or not to tune? Adapting pretrained representations to diverse tasks
Matthew Peters, Sebastian Ruder, and Noah A Smith. 2019 · 1903
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Building a large annotated corpus of English: The Penn treebank
Mitchell P. Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993 · 1993
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Supertagging: An approach to almost parsing
Srinivas Bangalore and Aravind K. Joshi. 1999 · 1999
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Introduction to the CoNLL-2000 shared task: Chunking
Erik F. Tjong Kim Sang and Sabine Buchholz. 2000 · 2000
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John D. Lafferty, Andrew McCallum, and Fernando Pereira. 2001 · 2001
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Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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CCGbank: A corpus of CCG derivations and dependency structures extracted from the Penn Treebank
Julia Hockenmaier and Mark Steedman. 2007 · 2007
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Factbank: a corpus annotated with event factuality
Roser Saurí and James Pustejovsky. 2009 · 2009
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A new dataset and method for automatically grading ESOL texts
Helen Yannakoudakis, Ted Briscoe, and Ben Medlock. 2011 · 2011
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Did it happen? The pragmatic complexity of veridicality assessment
Marie-Catherine de Marneffe, Christopher D. Manning, and Christopher Potts. 2012 · 2012
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CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012 · 2012
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Are you sure that this happened? Assessing the factuality degree of events in text
Roser Saurí and James Pustejovsky. 2012 · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, and Phillipp Koehn. 2014 · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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A gold standard dependency corpus for English
Natalia Silveira, Timothy Dozat, Marie-Catherine de Marneffe, Samuel Bowman, Miriam Connor, John Bauer, and Christopher D. Manning. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Visualizing and understanding recurrent networks
Andrej Karpathy, Justin Johnson, and Li Fei-Fei. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Lei Ba. 2015 · 2015
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Visualizing and understanding neural models in nlp
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2015 · 2015
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SemEval 2015 task 18: Broad-coverage semantic dependency parsing
Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Silvie Cinková, Dan Flickinger, Jan Hajic, and Zdenka Uresova. 2015 · 2015
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Evaluation of word vector representations by subspace alignment
Yulia Tsvetkov, Manaal Faruqui, Wang Ling, Guillaume Lample, and Chris Dyer. 2015 · 2015
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Semantic tagging with deep residual networks
Johannes Bjerva, Barbara Plank, and Johan Bos. 2016 · 2016
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Deep biaffine attention for neural dependency parsing
Timothy Dozat and Christopher D. Manning. 2016 · 2016
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Coordination annotation extension in the Penn Treebank
Jessica Ficler and Yoav Goldberg. 2016 · 2016
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LSTM CCG parsing
Mike Lewis, Kenton Lee, and Luke Zettlemoyer. 2016 · 2016
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Assessing the ability of LSTMs to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, Germán Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Simpler but more accurate semantic dependency parsing
Timothy Dozat and Christopher D. Manning. 2018 · 2018
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What’s going on in neural constituency parsers? An analysis
David Gaddy, Mitchell Stern, and Dan Klein. 2018 · 2018
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AllenNLP: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew E. Peters, Michael Schmitz, and Luke Zettlemoyer. 2018 · 2018
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Compositional sequence labeling models for error detection in learner writing
Marek Rei and Helen Yannakoudakis. 2016 · 2016
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The parallel meaning bank: Towards a multilingual corpus of translations annotated with compositional meaning representations
Lasha Abzianidze, Johannes Bjerva, Kilian Evang, Hessel Haagsma, Rik van Noord, Pierre Ludmann, Duc-Duy Nguyen, and Johan Bos. 2017 · 2017
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2017 · 2017
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Simulating action dynamics with neural process networks
Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, and Yejin Choi. 2017 · 2017
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
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A joint many-task model: Growing a neural network for multiple nlp tasks
Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher. 2017 · 2017
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Jeremy Howard and Sebastian Ruder. 2018 · 2018
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Visualisation and ‘diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure
Dieuwke Hupkes, Sara Veldhoen, and Willem Zuidema. 2018 · 2018
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Do language models understand anything? On the ability of LSTMs to understand negative polarity items
Jaap Jumelet and Dieuwke Hupkes. 2018 · 2018
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Sharp nearby, fuzzy far away: How neural language models use context
Urvashi Khandelwal, He He, Peng Qi, and Dan Jurafsky. 2018 · 2018
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What can linguistics and deep learning contribute to each other?
Tal Linzen. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Neural models of factuality
Rachel Rudinger, Aaron Steven White, and Benjamin Van Durme. 2018 · 2018
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Comprehensive supersense disambiguation of English prepositions and possessives
Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah R. Moeller, Aviram Stern, Adi Bitan, and Omri Abend. 2018 · 2018
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Why self-attention? A targeted evaluation of neural machine translation architectures
Gongbo Tang, Mathias Müller, Annette Rios, and Rico Sennrich. 2018 · 2018
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What do RNN language models learn about filler-gap dependencies?
Ethan Wilcox, Roger Levy, Takashi Morita, and Richard Futrell. 2018 · 2018
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Robust multilingual part-of-speech tagging via adversarial training
Michihiro Yasunaga, Jungo Kasai, and Dragomir R. Radev. 2018 · 2018
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Language modeling teaches you more syntax than translation does: Lessons learned through auxiliary task analysis
Kelly W. Zhang and Samuel R. Bowman. 2018 · 2018
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Analysis methods in neural language processing: A survey
Yonatan Belinkov and James Glass. 2019 · 2019
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Do RNNs learn human-like abstract word order preferences?
Richard Futrell and Roger P. Levy. 2019 · 2019
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Jointly learning to label sentences and tokens
Marek Rei and Anders Sogaard. 2019 · 2019
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Cross-lingual alignment of contextual word embeddings, with applications to zero-shot dependency parsing
Tal Schuster, Ori Ram, Regina Barzilay, and Amir Globerson. 2019 · 2019
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What do you learn from context? Probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R. Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
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