[1] BEN-NUN T,JAKOBOVITS A S,HOEFLER T.Neural code comprehension:a learnable representation of code semantics[C]//Proceedings of the 32nd International Con-ference on Neural Information Processing Systems.New York,USA:ACM Press,2018:3585-3597. [2] ALLAMANIS M,TARLOW D,GORDON A,et al.Bimodal modelling of source code and natural language[C]//Proceedings of the 32nd International Conference on Machine Learning.New York,USA:ACM Press,2015:2123-2132. [3] LIU Yang.Recent advances in neural machine translation[J].Journal of Computer Research and Development,2017,54(6):1144-1149.(in Chinese)刘洋.神经机器翻译前沿进展[J].计算机研究与发展,2017,54(6):1144-1149. [4] SUTSKEVER I,VINYALS O,LE Q V.Sequence to sequence learning with neural networks[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems.New York,USA:ACM Press,2014:3104-3112. [5] WU Renshou,WANG Hongling,WANG Zhongqing,et al.Short text summary generation with global self-matching mechanism[J].Journal of Software,2019,30(9):2705-2717.(in Chinese)吴仁守,王红玲,王中卿,等.全局自匹配机制的短文本摘要生成方法[J].软件学报,2019,30(9):2705-2717. [6] MING Tuosiyu,CHEN Hongchang,HUANG Ruiyang,et al.Semantic subgraph predictive summary algorithm based on weighted AMR graph[J].Computer Engineering, 2018,44(10):292-297,302.(in Chinese)明拓思宇,陈鸿昶,黄瑞阳,等.基于加权AMR图的语义子图预测摘要算法[J].计算机工程,2018,44(10):292-297,302. [7] GAMBHIR M,GUPTA V.Recent automatic text summariza-tion techniques:a survey[J].Artificial Intelligence Review,2017,47(1):1-66. [8] VINYALS O,FORTUNATO M,JAITLY N.Pointer networks[EB/OL].[2019-12-20].https://arxiv.org/pdf/1506.03134.pdf. [9] TAI K S,SOCHER R,MANNING C D.Improved semantic representations from tree-structured long short-term memory networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.New York,USA:ACM Press,2015:1556-1566. [10] HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780. [11] MOVSHOVITZ-ATTIAS D,COHEN W.Natural language models for predicting programming comments[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics.New York,USA:ACM Press,2013:35-40. [12] IYER S,KONSTAS I,CHEUNG A,et al.Summarizing source code using a neural attention model[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.New York,USA:ACM Press,2016:2073-2083. [13] ALLAMANIS M,PENG H,SUTTON C.A convolu-tional attention network for extreme summarization of source code[C]//Proceedings of International Conference on Machine Learning.New York,USA:ACM Press,2016:2091-2100. [14] WAN Yao,ZHAO Zhou,YANG Min,et al.Improving auto-matic source code summarization via deep reinforcement learning[C]//Proceedings of the 33rd ACM/IEEE Inter-national Conference on Automated Software Engineering.New York,USA:ACM Press,2018:397-407. [15] HU Xing,LI Ge,XIA Xin,et al.Deep code comment generation[C]//Proceedings of the 26th Conference on Program Comprehension.New York,USA:ACM Press,2018:200-210. [16] XU Kun,WU Lingfei,WANG Zhiguo,et al.Graph2Seq:graph to sequence learning with attention-based neural networks[EB/OL].[2019-12-20].https://arxiv.org/pdf/1804.00823.pdf. [17] SEE A,LIU P J,MANNING C D.Get to the point:summarization with pointer-generator networks[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.New York,USA:ACM Press,2017:1073-1083. [18] GU Jiatao,LU Zhengdong,LI Hang,et al.Incorporating copying mechanism in sequence-to-sequence learning[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.New York,USA:ACM Press,2016:1631-1640. [19] WANG L,BLUNSOM P,GREFENSTETTE E,et al.Latent predictor networks for code generation[C]//Proceedings of the 54th Annual Meeting of the Association for Computa-tional Linguistics.New York,USA:ACM Press,2016:599-609. [20] LIANG Y D,ZHU K Q.Automatic generation of text descriptive comments for code blocks[EB/OL].[2019-12-20].http://www.cs.sjtu.edu.cn/~kzhu/papers/kzhu-aaai18-code.pdf. [21] LUONG M T,PHAM H,MANNING C D.Effective approaches to attention-based neural machine translation[C]//Proceedings of 2015 Conference on Empirical Methods in Natural Language Processing.Washington D.C.,USA:IEEE Press,2015:1412-1421. [22] ZHONG V,XIONG C M,SOCHER R.Seq2SQL:generating structured queries from natural language using reinforcement learning[EB/OL].[2019-12-20].https://arxiv.org/pdf/1709.00103.pdf. [23] DONG L,LAPATA M.Language to logical form with neural attention[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.New York,USA:ACM Press,2016:33-47. [24] YIN P C,NEUBIG G.A syntactic neural model for general-purpose code generation[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.New York,USA:ACM Press,2017:440-458. [25] RABINOVICH M,STERN M,KLEIN D.Abstract syntax networks for code generation and semantic parsing[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.New York,USA:ACM Press,2017:1139-1153. [26] ERIGUCHI A,HASHIMOTO K,TSURUOKA Y.Tree-to-sequence attentional neural machine translation[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.New York,USA:ACM Press,2016:823-846. [27] PAPINENI K,ROUKOS S,WARD T,et al.BLEU:a method for automatic evaluation of machine translation[C]//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics.New York,USA:ACM Press,2002:311-318. [28] LIN C Y.Rouge:a package for automatic evaluation of summaries[EB/OL].[2019-12-20].https://www.aclweb.org/anthology/W04-1013.pdf. [29] KINGMA D P,BA J.Adam:a method for stochastic optimization[EB/OL].[2019-12-20].https://arxiv.org/pdf/1412.6980.pdf. [30] MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[EB/OL].[2019-12-20].https://arxiv.org/pdf/1301.3781.pdf. [31] PENNINGTON J,SOCHER R,MANNING C.Glove:global vectors for word representation[EB/OL].[2019-12-20].https://nlp.stanford.edu/pubs/glove.pdf. [32] GLOROT X,BENGIO Y.Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of the 13th International Conference on Artificial Intelligence and Statistics.New York,USA:ACM Press,2010:249-256. |