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While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations.
Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, William W Cohen, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov. 2019 · 1901
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Fair is better than sensational: Man is to doctor as woman is to doctor
Malvina Nissim, Rik van Noord, and Rob van der Goot. 2019 · 1905
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019 · 1906
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 1908
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Efficient sentence embedding using discrete cosine transform
Nada Almarwani, Hanan Aldarmaki, and Mona Diab. 2019 · 1909
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A Primer in BERTology: What we know about how BERT works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2002
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Corpus-based learning of analogies and semantic relations
Peter D. Turney and Michael L. Littman. 2005 · 2005
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Linguistic regularities in continuous space word representations
Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. 2013b · 2013
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Linguistic regularities in sparse and explicit word representations
Omer Levy and Yoav Goldberg. 2014 · 2014
Earlier work this paper cites.
Semeval-2014 task 1: Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment
Marco Marelli, Luisa Bentivogli, Marco Baroni, Raffaella Bernardi, Stefano Menini, and Roberto Zamparelli. 2014 · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus. 2014 · 2014
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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
Cited alongside, same era.
Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2016 · 2016
Cited alongside, same era.
Issues in evaluating semantic spaces using word analogies
Tal Linzen. 2016 · 2016
Cited alongside, same era.
Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
Cited alongside, same era.
Assessing composition in sentence vector representations
Allyson Ettinger, Ahmed Elgohary, Colin Phillips, and Philip Resnik. 2018 · 2018
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An efficient framework for learning sentence representations
Lajanugen Logeswaran and Honglak Lee. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Concatenated power mean word embeddings as universal cross-lingual sentence representations
Andreas Rücklé, Steffen Eger, Maxime Peyrard, and Iryna Gurevych. 2018 · 2018
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The Word Analogy Testing Caveat
Natalie Schluter. 2018 · 2018
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Matteo Pagliardini, Prakhar Gupta, and Martin Jaggi. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Probing sentence embeddings for structure-dependent tense
Geoff Bacon and Terry Regier. 2018 · 2018
Cited alongside, same era.
Daniel Cer, Yinfei Yang, Sheng yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2018 · 2018
Cited alongside, same era.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
Cited alongside, same era.
Evaluating compositionality in sentence embeddings
Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller, Samuel J Gershman, and Noah D Goodman. 2018 · 2018
Cited alongside, same era.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a
Cited in the paper.
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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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018a · 2018
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Exploring semantic properties of sentence embeddings
Xunjie Zhu, Tingfeng Li, and Gerard de Melo. 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. 2019 · 2019
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Verb argument structure alternations in word and sentence embeddings
Katharina Kann, Alex Warstadt, Adina Williams, and Samuel R Bowman. 2019 · 2019
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 2019
Later among the works it cites.