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Pretraining sentence encoders with language modeling and related unsupervised tasks has recently been shown to be very effective for language understanding tasks.
Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
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The Winograd schema challenge
Hector J Levesque, Ernest Davis, and Leora Morgenstern. 2011 · 2011
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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, Phillipp Koehn, and Tony Robinson. 2013 · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Retrofitting word vectors to semantic lexicons
Manaal Faruqui, Jesse Dodge, Sujay Kumar Jauhar, Chris Dyer, Eduard Hovy, and Noah A. Smith. 2015 · 2015
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Identifying beneficial task relations for multi-task learning in deep neural networks
Joachim Bingel and Anders Søgaard. 2017 · 2017
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Semeval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio, and Lucia Specia. 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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AllenNLP: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. 2017 · 2017
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Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017 · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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When does deep multi-task learning work for loosely related document classification tasks?
Emma Kerinec, Chloé Braud, and Anders Søgaard. 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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Learning general purpose distributed sentence representations via large scale multi-task learning
Sandeep Subramanian, Adam Trischler, Yoshua Bengio, and Christopher J. Pal. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2018 · 2018
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Neural network acceptability judgments
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Inference is everything: Recasting semantic resources into a unified evaluation framework
Aaron Steven White, Pushpendre Rastogi, Kevin Duh, and Benjamin Van Durme. 2017 · 2017
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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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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018a
Cited in the paper.
Dissecting contextual word embeddings: Architecture and representation
Matthew E Peters, Mark Neumann, Luke Zettlemoyer, and Wen-tau Yih. 2018b
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015a
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Alex Warstadt, Amanpreet Singh, and Samuel R Bowman. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Language modeling teaches you more syntax than translation does: Lessons learned through auxiliary task analysis
Kelly Zhang and Samuel R. Bowman. 2018 · 2018
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Looking for ELMo’s friends: Sentence-level pretraining beyond language modeling
Samuel R. Bowman, Ellie Pavlick, Edouard Grave, Benjamin Van Durme, Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, and Berlin Chen. 2019 · 2019
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