Pytorch学习记录-第二部分Text maching

语法纠错部分的推荐文献简单过了一遍，最强的肯定是微软GEC“流畅度提升学习和推断机制”，但是复现应该是比较困难的，而且还要和文本匹配模块进行整合。
关于Text Matching的论文，还得慢慢搜一下，在CSDN上看到的一个比较全的是这篇，目前还没有看，不知道质量如何，自己又在arxiv上面搜了2018年的一部分，继续刷论文……

2013

• Huang, Po Sen , et al. "Learning deep structured semantic models for web search using clickthrough data." Proceedings of the 22nd ACM international conference on Conference on information & knowledge management ACM, 2013. [ paper ]
• Lu, Zhengdong , and H. Li . "A Deep Architecture for Matching Short Texts. " International Conference on Neural Information Processing Systems Curran Associates Inc. 2013. [ paper ]
  2014
• Shen, Yelong , et al. " [ACM Press the 23rd ACM International Conference - Shanghai, China (2014.11.03-2014.11.07)] Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM "14 - A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval." (2014):101-110. [ paper ]

2015

• Hu, Baotian , et al. "Convolutional Neural Network Architectures for Matching Natural Language Sentences." (2015).[ paper ]
• Palangi, Hamid , et al. "Deep Sentence Embedding Using the Long Short Term Memory Network: - Analysis and Application to Information Retrieval." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24.4(2015). [ paper ]
• Severyn, Aliaksei , and A. Moschitti . "Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks." International Acm Sigir Conference ACM, 2015. [ paper ]
  Feng, Minwei , et al. "Applying Deep Learning to Answer Selection: A Study and An Open Task." (2015). [ paper ] [ 论文笔记 ]
• Yin, Wenpeng, and Hinrich Schütze. "Convolutional neural network for paraphrase identification." Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.[ paper ]
• Tan, Ming, et al. "LSTM-based deep learning models for non-factoid answer selection." arXiv preprint arXiv:1511.04108(2015). [ paper ] [ 论文笔记 ]
• He, Hua, Kevin Gimpel, and Jimmy Lin. "Multi-perspective sentence similarity modeling with convolutional neural networks." Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015. [ paper ] [ 论文笔记 ]

2016

• Pang, Liang , et al. "Text Matching as Image Recognition." (2016). [ paper ]
• Wan, Shengxian , et al. "Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN." Computers & Graphics28.5(2016):731-745. [ paper ]
• Yang, Liu, et al. "aNMM: Ranking short answer texts with attention-based neural matching model." Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016. [ paper ]
• Yin, Wenpeng, et al. "Abcnn: Attention-based convolutional neural network for modeling sentence pairs." Transactions of the Association for Computational Linguistics 4 (2016): 259-272. [ paper ]
  Wang, Shuohang, and Jing Jiang. "A compare-aggregate model for matching text sequences." arXiv preprint arXiv:1611.01747 (2016). [ paper ] [ 论文笔记 ]
• Rao, Jinfeng, Hua He, and Jimmy Lin. "Noise-contrastive estimation for answer selection with deep neural networks." Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016. [ paper ]
• Wang, Bingning, Kang Liu, and Jun Zhao. "Inner attention based recurrent neural networks for answer selection." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vol. 1. 2016. [ paper ]

2017

• Mitra, Bhaskar, Fernando Diaz, and Nick Craswell. "Learning to match using local and distributed representations of text for web search." Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017. [ paper ]
  Xiong, Chenyan, et al. "End-to-end neural ad-hoc ranking with kernel pooling." Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2017. [ paper ]
• Mitra, Bhaskar, and Nick Craswell. "Neural Models for Information Retrieval." arXiv preprint arXiv:1705.01509(2017). [ paper ]
• Wang, Zhiguo, Wael Hamza, and Radu Florian. "Bilateral multi-perspective matching for natural language sentences." arXiv preprint arXiv:1702.03814 (2017). [ paper ] [ 论文笔记 ]
• Min, Sewon, Minjoon Seo, and Hannaneh Hajishirzi. "Question answering through transfer learning from large fine-grained supervision data." arXiv preprint arXiv:1702.02171(2017). [ paper ]

2018

• Liu, Bang, et al. "Matching Natural Language Sentences with Hierarchical Sentence Factorization." arXiv preprint arXiv:1803.00179 (2018). [ paper ]
• Qiu, Minghui, et al. "Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce." arXiv preprint arXiv:1806.05434 (2018). [ paper ]
• McCann, Bryan, et al. "The natural language decathlon: Multitask learning as question answering." arXiv preprint arXiv:1806.08730 (2018). [ paper ]
• Tay, Y., L.A. Tuan and S.C. Hui, Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences. 2018.
• Brahma, S., REGMAPR - Text Matching Made Easy. 2018.
• Chen, D., et al., Identifying High-Quality Chinese News Comments Based on Multi-Target Text Matching Model. 2018.
• Liu, B., et al., Matching Long Text Documents via Graph Convolutional Networks. 2018.
• Mozer, R., et al., Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality. 2018.
• Qu, C., et al., Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching. 2018.