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We consider the problem of learning general-purpose, paraphrastic sentence embeddings, revisiting the setting of Wieting et al.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Extracting paraphrases from a parallel corpus
Regina Barzilay and Kathleen R McKeown. 2001 · 2001
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Learning precise timing with LSTM recurrent networks
Felix A. Gers, Nicol N. Schraudolph, and Jürgen Schmidhuber. 2003 · 2003
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Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources
Bill Dolan, Chris Quirk, and Chris Brockett. 2004 · 2004
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Vector-based models of semantic composition
Jeff Mitchell and Mirella Lapata. 2008 · 2008
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Composition in distributional models of semantics
Jeff Mitchell and Mirella Lapata. 2010 · 2010
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Simple english wikipedia: a new text simplification task
William Coster and David Kauchak. 2011 · 2011
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Dynamic pooling and unfolding recursive autoencoders for paraphrase detection
Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, and Christopher D. Manning. 2011 · 2011
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SemEval-2012 task 6: A pilot on semantic textual similarity
Eneko Agirre, Mona Diab, Daniel Cer, and Aitor Gonzalez-Agirre. 2012 · 2012
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A comparison of vector-based representations for semantic composition
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Semantic compositionality through recursive matrix-vector spaces
Richard Socher, Brody Huval, Christopher D. Manning, and Andrew Y. Ng. 2012 · 2012
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*SEM 2013 shared task: Semantic textual similarity
Eneko Agirre, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, and Weiwei Guo. 2013 · 2013
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PPDB: The Paraphrase Database
Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch. 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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SemEval-2014 task 10: Multilingual semantic textual similarity
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2014 · 2014
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The multilingual paraphrase database
Juri Ganitkevitch and Chris Callison-Burch. 2014 · 2014
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Convolutional neural network architectures for matching natural language sentences
Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. 2014 · 2014
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Deep recursive neural networks for compositionality in language
Ozan İrsoy and Claire Cardie. 2014 · 2014
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Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014 · 2014
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Yoon Kim. 2014 · 2014
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Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Finding function in form: Compositional character models for open vocabulary word representation
Wang Ling, Chris Dyer, Alan W Black, Isabel Trancoso, Ramon Fermandez, Silvio Amir, Luis Marujo, and Tiago Luis. 2015 · 2015
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Multi-timescale long short-term memory neural network for modelling sentences and documents
Pengfei Liu, Xipeng Qiu, Xinchi Chen, Shiyu Wu, and Xuanjing Huang. 2015 · 2015
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Jointly optimizing word representations for lexical and sentential tasks with the c-phrase model
Nghia The Pham, Germán Kruszewski, Angeliki Lazaridou, and Marco Baroni. 2015 · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015 · 2015
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Distributed representations of sentences and documents
Quoc V. Le and Tomas Mikolov. 2014 · 2014
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The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
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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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Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 2014
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SemEval-2015 task 2: Semantic textual similarity, English, Spanish and pilot on interpretability
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Inigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, and Janyce Wiebe. 2015 · 2015
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Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. 2015 · 2015
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From paraphrase database to compositional paraphrase model and back
John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu, and Dan Roth. 2015 · 2015
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SemEval-2015 task 1: Paraphrase and semantic similarity in Twitter (PIT)
Wei Xu, Chris Callison-Burch, and William B Dolan. 2015 · 2015
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Semeval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation
Eneko Agirre, Carmen Banea, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2016 · 2016
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Learning distributed representations of sentences from unlabelled data
Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016 · 2016
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Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team. 2016 · 2016
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Charagram: Embedding words and sentences via character n n -grams
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2016a · 2016
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A simple but tough-to-beat baseline for sentence embeddings
Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2017 · 2017
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Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
Matteo Pagliardini, Prakhar Gupta, and Martin Jaggi. 2017 · 2017
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