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Text matching is a core natural language processing research problem.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean · 2013
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Distributed representations of sentences and documents
Quoc V. Le and Tomas Mikolov · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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From word embeddings to document distances
Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, and Kilian Q. Weinberger · 2015
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Learning to rank short text pairs with convolutional deep neural networks
Aliaksei Severyn and Alessandro Moschitti · 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
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Key-value memory networks for directly reading documents
Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston · 2016
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Siamese recurrent architectures for learning sentence similarity
Jonas Mueller and Aditya Thyagarajan · 2016
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A decomposable attention model for natural language inference
Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit · 2016
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Improved representation learning for question answer matching
Ming Tan, Cicero dos Santos, Bing Xiang, and Bowen Zhou · 2016
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ABCNN: Attention-based convolutional neural network for modeling sentence pairs
Wenpeng Yin, Hinrich Schütze, Bing Xiang, and Bowen Zhou · 2016
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Enhanced LSTM for natural language inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen · 2017
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Inter-weighted alignment network for sentence pair modeling
Gehui Shen, Yunlun Yang, and Zhi-Hong Deng · 2017
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Neural paraphrase identification of questions with noisy pretraining
Gaurav Singh Tomar, Thyago Duque, Oscar Täckström, Jakob Uszkoreit, and Dipanjan Das · 2017
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A compare-aggregate model for matching text sequences
Shuohang Wang and Jing Jiang · 2017
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Bilateral multi-perspective matching for natural language sentences
Zhiguo Wang, Wael Hamza, and Radu Florian · 2017
Improved text matching by enhancing mutual information
Yang Liu, Wenge Rong, and Zhang Xiong · 2018
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Multiway attention networks for modeling sentence pairs
Chuanqi Tan, Furu Wei, Wenhui Wang, Weifeng Lv, and Ming Zhou · 2018
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Co-stack residual affinity networks with multi-level attention refinement for matching text sequences
Yi Tay, Anh Tuan Luu, and Siu Cheung Hui · 2018
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Compare, compress and propagate: Enhancing neural architectures with alignment factorization for natural language inference
Yi Tay, Anh Tuan Luu, and Siu Cheung Hui · 2018
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Hermitian co-attention networks for text matching in asymmetrical domains
Yi Tay, Anh Tuan Luu, and Siu Cheung Hui · 2018
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Learning deep representations by mutual information estimation and maximization
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Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm · 2018
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Document similarity for texts of varying lengths via hidden topics
Hongyu Gong, Tarek Sakakini, Suma Bhat, and Jinjun Xiong · 2018
Cited alongside, same era.
Scitail: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark · 2018
Cited alongside, same era.
Stochastic answer networks for natural language inference
Xiaodong Liu, Kevin Duh, and Jianfeng Gao · 2018
Cited alongside, same era.
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Enhancing variational autoencoders with mutual information neural estimation for text generation
Dong Qian and William K. Cheung · 2019
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Learning disentangled representation for cross-modal retrieval with deep mutual information estimation
Guo Weikuo, Huaibo Huang, Xiangwei Kong, and Ran He · 2019
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Simple and effective text matching with richer alignment features
Runqi Yang, Jianhai Zhang, Xing Gao, Feng Ji, and Haiqing Chen · 2019
Later among the works it cites.