[1] 王宝鑫,郑德权,王晓雪,等.基于文本蕴含的选择类问题解答技术研究[J]. 北京大学学报(自然科学版),2016, 52(1),134-140.
[2] Li H R, Zhu J N, Zhang J J, et al. Ensure the correctness of the summary: Incorporate entailment knowledge into abstractive sentence summarization[C]//Proceedings of the 27th International Conference on Computational Linguistic, 2018:1430-1441.
[3] Dagan I,Glickman O, Magnini B. The Pascal recognising textual entailment challenge[C]//Proceedings of the International Conference on Machine Learning Challenges: Evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment, 2005: 177-190.
[4] Marelli M, Bentivogli L, Baroni M,et al. Semeval-2014 task1: Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment[C]//Proceedings of the 8th International Workshop on Semantic Evaluation, 2014:1-8.
[5] 刘茂福,李妍,姬东鸿.基于事件语义特征的中文文本蕴含识别[J]. 中文信息学报, 2013, 27(5):129-137.
[6] 张晗,盛雅琦,吕晨,等.基于短文本隐含语义特征的文本蕴涵识别[J]. 中文信息学报, 2016, 30(3):163-171.
[7] Bowman S R, Angeli G, Potts C, et al. A large annotated corpus for learning natural language inference[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015: 632-642.
[8] Williams A, Nangia N, Bowman S R. A broad-coverage challenge corpus for sentence understanding through inference[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics, 2018: 1112-1122.
[9] Nie Y, Bansal M. Shortcut-stacked sentence encodersfor multi-domain inference[C]//Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP. Copenhagen, Denmark: Association for Computational Linguistics, 2017:41-45.
[10] Mou L L, Li G, Zhang L,et al. Convolutional neural networks over tree structures for programming language processing[C]//Proceedings of the 30th AAAI Conference on Artifical Intelligence, 2016:1287-1293.
[11] Parikh A, Tackstrom O, Das D, et al. A decomposable attention model for natural language inference[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016: 2249-2255.
[12] Chen Q, Zhu X, Ling Z H,et al. Enhanced LSTM for natural language inference[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 1657-1668.
[13] Chen Q, Zhu X, Ling ZH,et al. Neural natural language inference models enhanced with external knowledge[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Melbourne, Australia: Association for Computational Linguistics, 2018: 2406-2417.
[14] Tay Y, Tuan L A, Hui S C. A compare propagate architecture with alignment factorization for natural language inference[C]//Proceedings of the CoRR, 2018.
[15] Rocktschel T, Grefenstette E, Hermann K M,et al. Reasoning about entailment with neural attention[C]//Proceedings of the International Conference on Learning Representations (ICLR),2016.
[16] Peters M E, Neumann M, Iyyer M,et al. Deep contextualized word representations[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistic, 2018:2227-2237.
[17] Wang Z G, Hamza W, Florian R. Bilateral multi-perspective matching for natural language sentences[C]//Proceedings of the International Joint Conference on Artificial Intelligence, 2017: 4144-4150.
[18] Miller G A. WordNet: A lexical database for English[J]. Communications of the ACM, 1995, 38(11):39-41.
[19] Vaswani A, Shazeer N, Parmar N,et al. Attention is all you need[C]//Proceedings of the 31st International Joint Conference or Neural Information Processing Systems, 2017:5998-6008.
[20] Devlin J, Chang M, Lee K,et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 2019: 4171-4186.
[21] Pennington J, Socher R, Manning C D. GloVe: Global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 2014: 1532-1543.
[22] Chen D, Fisch A, Weston J,et al. Reading Wikipedia to answer open-domain questions[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 1870-1879.
[23] Du Q L, Zong C Q, Su K Y. Integrating structural context with local context for disambiguation word sense[C]//Proceedings of the 5th Conference On Natural Language Processing and Chinese Computing & The 24th International Conference On Computer Processing of Oriental Languages, 2016: 3-15.
[24] Wang S, Jiang J . Learning natural language inference with LSTM[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016.
[25] Du Q L, Zong C Q, Su K Y. Adopting the word-pair-dependency-triplets with individual comparison for natural language inference[C]//Proceedings of the 27th International Conference on Computational Linguistics (COLING), Santa Fe, New-Mexico, USA, 2018: 414-425.
[26] Sha L, Chang B B, Sui Z F,et al. Reading and thinking: Reread LSTM unit for textual entailment recognition[C]//Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics, 2016: 2870-2879.
[27] Gong Y C, Luo H, Zhang J. Natural language inference over interaction space[C]//Proceedings of the International Conference on Learning Representations, 2018.
[28] Ghaeini R, Hasan S A, Datla V,et al. DR-BiLSTM: Dependent reading bidirectional LSTM for natural language inference[C]//Proceedings of the NAACL-HLT, 2018: 1460-1469.
[29] Chen Q, Zhu X D, Ling Z H, et al. Recurrent neural network-based sentence encoder with gated attention for natural language inference[C]//Proceedings of the 2nd Workshop on Evaluating Vector-Space Representation for NLP, 2017: 36-40.
[30] Balazs J A, Marrese-Taylor E, Loyola P,et al. Reining raw sentence representations for textual entailment recognition via attention[C]//Proceedings of the 2nd Workshop on Evaluating Vector-Space Representations for NLP, 2017: 51-55.