[1] KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg,PA:Association for Computational Linguistics,2014:1746-1751. [2] HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural Computation,1997,9(8):1735-1780. [3] VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook,NY:Curran Associates Inc.,2017:6000-6010. [4] SEO M, KEMBHAVI A, FARHADI A, et al. Bi-directional attention flow for machine comprehension[EB/OL]. (2018-06-21)[2020-05-15]. https://arxiv.org/pdf/1611.01603.pdf. [5] HUANG H Y,ZHU C G,SHEN Y L,et al. FusionNet:fusing via fully-aware attention with application to machine comprehension[EB/OL]. (2018-02-04)[2020-05-15]. https://arxiv.org/pdf/1711.07341.pdf. [6] YU A W,DOHAN D,LUONG M T,et al. QANet:combining local convolution with global self-attention for reading comprehension[EB/OL]. (2018-04-23)[2020-05-15]. https://arxiv.org/pdf/1804.09541.pdf. [7] SUN F, LI L Y, QIU X P, et al. U-Net:machine reading comprehension with unanswerable questions[EB/OL]. (2018-10-12)[2020-06-15]. https://arxiv.org/pdf/1810.06638.pdf. [8] WANG W, YAN M, WU C. Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics,2018:1705-1714. [9] LIU X D,SHEN Y L,DUH K,et al. Stochastic answer networks for machine reading comprehension[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2018:1694-1704. [10] RAJPURKAR P, ZHANG J, LOPYREV K, et al. SQuAD:100,000+ questions for machine comprehension of text[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg,PA:Association for Computational Linguistics,2016:2383-2392. [11] XIONG C M,ZHONG V,SOCHER R. DCN+:mixed objective and deep residual coattention for question answering[EB/OL]. (2017-11-10)[2020-05-15]. https://arxiv.org/pdf/1711.00106.pdf. [12] RAJPURKAR P,JIA R,LIANG P. Know what you don't know:unanswerable questions for SQuAD[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2018:784-789. [13] McCANN B,BRADBURY J,XIONG C M,et al. Learned in translation:contextualized word vectors[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook,NY:Curran Associates Inc.,2017:6297-6308. [14] JI J S,WANG Q L,TOUTANOVA K,et al. A nested attention neural hybrid model for grammatical error correction[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics,2017:753-762. [15] LIU R,HU J J,WEI W,et al. Structural embedding of syntactic trees for machine comprehension[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:Association for Computational Linguistics, 2017:815-824. [16] WANG X Y,XU G L,ZHANG J Y,et al. Syntax-directed hybrid attention network for aspect-level sentiment analysis[J]. IEEE Access,2019,7:5014-5025. [17] ZHANG Z S,WU Y W,ZHOU J R,et al. SG-Net:syntax-guided machine reading comprehension[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press,2020:9636-9643. [18] ZHANG X,ZHAO J B,LeCUN Y. Character-level convolutional networks for text classification[C]//Proceedings of the 28th Conference on Neural Information Processing Systems. Cambridge:MIT Press,2015:649-657. [19] BOJANOWSKI P,GRAVE E,JOULIN A,et al. Enriching word vectors with subword information[J]. Transactions of the Association for Computational Linguistics,2017,5:135-146. [20] PASZKE A, GROSS S, CHINTALA S, et al. Automatic differentiation in PyTorch[EB/OL].[2020-06-15]. https://openreview.net/pdf/25b8eee6c373d48b84e5e9c6e10e7cbbbce4ac73.pdf. |