董孝政,洪宇,朱芬红,姚建民,朱巧明. 基于密令位置信息特征的问题生成[J]. 中文信息学报, 2019, 33(8): 93-100.
Dong Xiaozheng, Hong Yu, Zhu Fenhong, Yao Jianmin, Zhu Qiaoming. Question Generation Based on Information Features of Token Position. , 2019, 33(8): 93-100.
基于密令位置信息特征的问题生成
董孝政,洪宇,朱芬红,姚建民,朱巧明
苏州大学 计算机科学与技术学院,江苏 苏州 215006
Question Generation Based on Information Features of Token Position
Dong Xiaozheng, Hong Yu, Zhu Fenhong, Yao Jianmin, Zhu Qiaoming
School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
Abstract：The question generation task aims to automatically generate one or more questions on the condition of understanding the semantics of a declarative sentence. This paper focuses on one of the sub-tasks, Point-wise Question Generation (PQG), and proposes a seq2seq PGQ model that combines attention mechanism about tokens. Among them, the token is a general summary of the potential answers for the sentences level, which is often shown as a series of consecutive terms in a declarative sentence. In terms of method implementation, the position information of the token and the semantic information of the whole sentence are integrated in the process of encoding. While in the process of decoding, the attention of token is strengthened. The experiment is carried out on the SQuAD corpus, revealing a better performance of 1.98% improvement in BLEU-4.
[1] Rus V,Wyse B,Piwek P,et al.The first question generation shared task evaluation challenge[C]//Proceedings of the 6th International Natural Language Generation Conference.Association for Computational Linguistics,2010: 251-257. [2] Zhou Q,Yang N,Wei F,et al.Neural question generation from text: A preliminary study[C]//Proceedings of National CCF Conference on Natural Language Processing and Chinese Computing.Springer,Cham,2017: 662-671. [3] 郑实福,刘挺,秦兵,等.自动问答综述[J].中文信息学报,2002,16(6):46-52. [4] 李舟军,李水华.基于Web的问答系统综述[J].计算机科学,2017,44(6):1-7. [5] 付文博,孙涛,梁藉,等.深度学习原理及应用综述[J].计算机科学,2018,45(6A):12-40. [6] Hochreiter S,Schmidhuber J.Long short-term memory[J].Neural Computation,1997,9(8): 1735-1780. [7] Kim Y.Convolutional neural networks forsentence classification[J].arXiv preprint arXiv:1408.5882,2014. [8] Rajpurkar Pranav,Zhang Jian,Lopyrev Konstant et al.SQuAD:100 000+questions for machine comprehension of text[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:2383-2392. [9] Heilman M,Smith N A.Good question! statistical ranking for question generation[C]//Proceedings of Human Language Technologies:The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics.Association for Computational Linguistics,2010:609-617. [10] Kutner M H,Nachtsheim C,Neter J.Applied linear regression models[M].McGraw-Hill/Irwin,2004:4-318. [11] Yao X.Generating more specific questions[M/OL].Question Generation,Papers from the 2011 {AAAI} Fall Symposium,Arlington,Virginia,USA,November 4-6,2011.[2011-11-04],http://www.aaai.org/Library/Symposia/Fall/fs11-04.php. [12] Liu M,Rus V,Liu L.Automatic Chinese factual question generation[J].IEEE Transactions on Learning Technologies,2017,10(2): 194-204. [13] Du X,Shao J,Cardie C.Learning to ask: Neural question generation for reading comprehension[J].arXiv preprint arXiv:1705.00106,2017. [14] Duan N,Tang D,Chen P,et al.Question generation for question answering[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing,2017: 866-874. [15] Greff K,Srivastava R K,Koutnkí J,et al.LSTM: A search space odyssey[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(10): 2222-2232. [16] Pennington J,Socher R,Manning C.Glove: Global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP),2014: 1532-1543. [17] Dong X,Hong Y,Chen X,et al.Neural question generation with semantics of question type[C]//Proceedings of CCF International Conference on Natural Language Processing and Chinese Computing.Springer,Cham,2018: 213-223. [18] Papineni K,Roukos S,Ward T,et al.BLEU: A method for automatic evaluation of machine translation[C]//Proceedings of the 40th Annual Meeting on Association for Computational Linguistics.Association for Computational Linguistics,2002: 311-318. [19] Denkowski M,Lavie A.Meteor universal: Language specific translation evaluation for any target language[C]//Proceedings of the 9th Workshop on Statistical Machine Translation.2014: 376-380. [20] Lin C Y.Rouge:A package for automatic evaluation of summaries[M].Text Summarization Branches Out,2004:74-81.
2019, Vol. 33 
