publication . Preprint . 2018

Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering

Lan, Wuwei; Xu, Wei;
Open Access English
  • Published: 12 Jun 2018
Abstract
Comment: 13 pages; accepted to COLING 2018
Subjects
free text keywords: Computer Science - Computation and Language
Related Organizations
Funded by
NSF| CRII: RI: Learning a Timely Semantic Resource from Social Media Data
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1755898
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
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40 references, page 1 of 3

[Agirre et al.2014] Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2014. Semeval-2014 task 10: Multilingual semantic textual similarity. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014).

[Agirre et al.2016] Eneko Agirre, Aitor Gonzalez-Agirre, Inigo Lopez-Gazpio, Montse Maritxalar, German Rigau, and Larraitz Uria. 2016. Semeval-2016 task 2: Interpretable semantic textual similarity. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval).

[Bowman et al.2015] Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP).

[Bowman et al.2016] Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D. Manning, and Christopher Potts. 2016. A fast unified model for parsing and sentence understanding. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL).

[Center2012] Ohio Supercomputer Center. 2012. Oakley supercomputer. http://osc.edu/ark:/19495/ hpc0cvqn.

[Chen et al.2017] Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017. Enhanced LSTM for natural language inference. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL).

[Choi et al.2017] Jihun Choi, Kang Min Yoo, and Sang-goo Lee. 2017. Unsupervised learning of task-specific tree structures with tree-LSTMs. arXiv preprint arXiv:1707.02786. [OpenAIRE]

[Conneau et al.2017] Alexis Conneau, Douwe Kiela, Holger Schwenk, Lo¨ıc Barrault, and Antoine Bordes. 2017. Supervised learning of universal sentence representations from natural language inference data. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP).

[Dagan et al.2006] Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006. The PASCAL recognising textual entailment challenge. In Proceedings of the First International Conference on Machine Learning Challenges: Evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment.

[Dolan and Brockett2005] William B Dolan and Chris Brockett. 2005. Automatically constructing a corpus of sentential paraphrases. In Proceedings of the Third International Workshop on Paraphrasing (IWP).

[Ghaeini et al.2018] Reza Ghaeini, Sadid A Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Z Fern, and Oladimeji Farri. 2018. DR-BiLSTM: Dependent reading bidirectional LSTM for natural language inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).

[Gong et al.2017] Yichen Gong, Heng Luo, and Jian Zhang. 2017. Natural language inference over interaction space. arXiv preprint arXiv:1709.04348.

[He and Lin2016] Hua He and Jimmy Lin. 2016. Pairwise word interaction modeling with deep neural networks for semantic similarity measurement. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).

[He et al.2015] Hua He, Kevin Gimpel, and Jimmy Lin. 2015. Multi-perspective sentence similarity modeling with convolutional neural networks. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP).

[Hill et al.2016] Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016. Learning distributed representations of sentences from unlabelled data. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).

40 references, page 1 of 3
Abstract
Comment: 13 pages; accepted to COLING 2018
Subjects
free text keywords: Computer Science - Computation and Language
Related Organizations
Funded by
NSF| CRII: RI: Learning a Timely Semantic Resource from Social Media Data
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1755898
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
Download from
40 references, page 1 of 3

[Agirre et al.2014] Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2014. Semeval-2014 task 10: Multilingual semantic textual similarity. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014).

[Agirre et al.2016] Eneko Agirre, Aitor Gonzalez-Agirre, Inigo Lopez-Gazpio, Montse Maritxalar, German Rigau, and Larraitz Uria. 2016. Semeval-2016 task 2: Interpretable semantic textual similarity. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval).

[Bowman et al.2015] Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP).

[Bowman et al.2016] Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D. Manning, and Christopher Potts. 2016. A fast unified model for parsing and sentence understanding. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL).

[Center2012] Ohio Supercomputer Center. 2012. Oakley supercomputer. http://osc.edu/ark:/19495/ hpc0cvqn.

[Chen et al.2017] Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017. Enhanced LSTM for natural language inference. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL).

[Choi et al.2017] Jihun Choi, Kang Min Yoo, and Sang-goo Lee. 2017. Unsupervised learning of task-specific tree structures with tree-LSTMs. arXiv preprint arXiv:1707.02786. [OpenAIRE]

[Conneau et al.2017] Alexis Conneau, Douwe Kiela, Holger Schwenk, Lo¨ıc Barrault, and Antoine Bordes. 2017. Supervised learning of universal sentence representations from natural language inference data. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP).

[Dagan et al.2006] Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006. The PASCAL recognising textual entailment challenge. In Proceedings of the First International Conference on Machine Learning Challenges: Evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment.

[Dolan and Brockett2005] William B Dolan and Chris Brockett. 2005. Automatically constructing a corpus of sentential paraphrases. In Proceedings of the Third International Workshop on Paraphrasing (IWP).

[Ghaeini et al.2018] Reza Ghaeini, Sadid A Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Z Fern, and Oladimeji Farri. 2018. DR-BiLSTM: Dependent reading bidirectional LSTM for natural language inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).

[Gong et al.2017] Yichen Gong, Heng Luo, and Jian Zhang. 2017. Natural language inference over interaction space. arXiv preprint arXiv:1709.04348.

[He and Lin2016] Hua He and Jimmy Lin. 2016. Pairwise word interaction modeling with deep neural networks for semantic similarity measurement. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).

[He et al.2015] Hua He, Kevin Gimpel, and Jimmy Lin. 2015. Multi-perspective sentence similarity modeling with convolutional neural networks. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP).

[Hill et al.2016] Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016. Learning distributed representations of sentences from unlabelled data. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).

40 references, page 1 of 3
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