计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 278-285.doi: 10.11896/jsjkx.210900250
吴俣1, 李舟军2
WU Yu1, LI Zhou-jun2
摘要： 随着自然语言处理技术的飞速发展以及互联网上对话语料的不断积累,闲聊导向对话系统(简称聊天机器人)取得了令人瞩目的进展,受到了学术界的广泛关注,并在产业界进行了初步的尝试。当前,聊天机器人分为检索式聊天机器人和生成式聊天机器人,而检索式聊天机器人由于其生成的回复流畅且计算资源消耗小,仍然是目前工业界聊天机器人的主要实现手段。文中首先简要介绍了检索式聊天机器人的研究背景、基本架构以及组成模块,重点阐述了回复选择模块的约束要求和相关数据集;然后,针对检索式聊天机器人中最为核心的回复选择技术,进行了深入分析与详细梳理。文中将近年来经典的回复选择技术归纳为如下4类:基于统计模型的方法、基于表示的神经网络模型的方法、基于交互的神经网络模型的方法以及基于预训练技术的方法,并指出了这4类方法的优点和不足。在此基础上,分析了目前检索式聊天机器人技术研究所面临的问题,并对其未来的发展趋势进行了展望。
中图分类号:
| [1]WEIZENBAUM J.ELIZA:a computer program for the study of natural language communication between man and machine[J].Communications of the ACM,1966,9(1):36-45. [2]RITTER A,CHERRY C,DOLAN W B.Data-driven response generation in social media[C]//Proceedings of the 2011 Confe-rence on Empirical Methods in Natural Language Processing.2011:583-593. [3]JI Z,LU Z,LI H.An Information Retrieval Approach to Short Text Conversation[J].arXiv:1408.6988,2014. [4]VINYALS O,LE Q.A neural conversational model[J].arXiv:1506.05869,2015. [5]SUTSKEVER I,VINYALS O,LE Q V.Sequence to sequence learning with neural networks[C]//Advances in Neural Information Processing Systems.2014:3104-3112. [6]ZHOU L,GAO J,LI D,et al.The design and implementation of xiaoice,an empathetic social chatbot.[J].Computational Linguistics,2020,46(1):53-93. [7]LOWE R,POW N,SERBAN I,et al.The Ubuntu Dialogue Corpus:A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems[C]//Proceedings of the SIGDIAL 2015 Conference,The 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue.Prague,Czech Republic,2015:285-294. [8]WANG H,LU Z,LI H,et al.A Dataset for Research on Short-Text Conversations[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.EMNLP,2013:935-945. [9]WU Y,WU W,XING C,et al.Sequential Match Network:A New Architecture for Multi-turn Response Selection in Re-trieval-based Chatbots[C]//Proceedings of the 55th Annual Mee-ting of the Association for Computational Linguistics.2017:496-505. [10]ZHANG Z,LI J,ZHU P,ZHAO H,LIU G.Modeling Multi-turn Conversation with Deep Utterance Aggregation[C]//Proceedings of the 27th International Conference on Computational Linguistics 2018:3740-3752. [11]ZHANG S,DINAN E,URBANEK J,et al.Personalizing Dialogue Agents:I have a dog,do you have pets too?[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.ACL,2018:2204-2213. [12]WELLECK S,WESTON J,SZLAM A,et al.Dialogue natural language inference [C]//Proceedings of the 57th Annual Mee-ting of the Association for Computational Linguistics.ACL,2019:3731-3741. [13]BURGES C,SHAKED T,RENSHAW E,et al. Learning to rank using gradient descent[C]//Proceedings of the 22nd International Conference on Machine Learning (ICML-05):89-96. [14]SPÄRCK J K.A Statistical Interpretation of Term Specificity and Its Application in Retrieval[J].Journal of Documentation,1972;28(1):11-21. [15]BROWN P F,DELLA S A,DELLA P V J,et al.The mathema- tics of statistical machine translation:Parameter estimation [J].Computational Linguistics,1993;19(2):263-311. [16]ZHAO X,JIANG J,WENG J,et al.Comparing twitter and traditional media using topic models[C]//European Conference on Information Retrieval.2011:338-349. [17]WAGNER R A,FISCHER M J.The string-to-string correction problem[J].Journal of the ACM (JACM),1974,21(1):168-173. [18]MACKAY D J.Information theory,inference and learning algorithms [M].Cambridge University Press,2003. [19]BAEZA-YATES R,RIBEIRO-NETO B,et al.Modern information retrieval[M]//volume 463.ACM press,New York,1999. [20]CHOI J,YOO K,LEE S.Learning to compose task-specific tree structures[C]//Thirty-Second AAAI Conference on Artificial Intelligence.2018:248-258. [21]LIU X,KEVIN D,GAO J.Stochastic answer networks for natural language inference[J].arXiv:1804.07888,2018. [22]KALCHBRENNER N,GREFENSTETTE E,BLUNSOM.A Convolutional Neural Network for Modelling Sentences[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.2014:655-665. [23]ZHOU X,DONG D,WU H,et al.Multi-view Response Selection for Human-Computer Conversation[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:372-381. [24]YAN R,SONG Y,WU H.Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval.2016:55-64. [25]QIU X,HUANG X.Convolutional neural tensor network architecture for community-based question answering[C]//Procee-dings of the 24th International Conference on Artificial Intelligence.2015:1305-1311. [26]WU Y,WU W,XING C,et al.A sequential matching framework for multi-turn response selection in retrieval-based chatbots [J].Computational Linguistics,2019,45(1),163-197. [27]ZHOU X,LI L,DONG D,et al.Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers).2018:1118-1127. [28]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Advances in Neural Information Processing Systems.2017:5998-6008. [29]YIN W,SCHÜTZE H,XIANG B,et al.Abcnn:Attention-based convolutional neural network for modeling sentence pairs[J].Transactions of the Association for Computational Linguistics,2016(4):259-272. [30]PANG L,LAN Y,GUO J,et al.Text matching as image recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2016:2793-2799. [31]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 (Volume 1:Long Papers).2017:1657-1668. [32]SONG S,WANG C,PU X,et al.An Enhanced Convolutional Inference Model with Distillation for Retrieval-Based QA[C]//DASFAA.2021:511-515. [33]PETERS M,NEUMANN M,IYYER M,et al.Deep contextua- lized word representations[C]//Proceedings of NAACL-HLT.2018:2227-2237. [34]MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]//Advances in Neural Information Processing Systems.2013:3111-3119. [35]RADFORD A,NARASIMHAN K,SALIMANS T,et al.Improving language understanding by generative pre-training[OL].https://s3-us-west-2.amazonaws.com/openai-assets/researchcovers/languageunsupervised/language understanding paper.pdf,2018. [36]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding [C]//Proceedings of NAACL-HLT.2019:4171-4186. [37]LIU Y,OTT M,GOYAL N,et al.Roberta:A robustly opti- mized BERT pretraining approach[J].arXiv:1907.11692,2019. [38]YANG Z,DAI Z,YANG Y,et al.Xlnet:Generalized autoregressive pretraining for language understanding[C]//Advances in Neural Information Processing Systems.2019:32-42. [39]ZHANG Z,HAN X,LIU Z,et al.ERNIE:Enhanced Language Representation with Informative Entities[J].arXiv:1905.07129,2019. [40]WHANG T,LEE D,LEE C,et al.Domain Adaptive Training BERT for Response Selection[J].arXiv:1908.04812. [41]TALMOR A,HERZIG J,LOURIE N,et al.Commonsense QA:A Question Answering Challenge Targeting Commonsense Knowledge[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2019:4149-4158. [42]DUA D,WANG Y,DASIGI P,et al.DROP:A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs[C]//Proceedings of North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2019:2368-2378. [43]ZHOU K,ZHANG K,WU Y,et al.Unsupervised Context Rewriting for Open Domain Conversation[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing.2017:1834-1844. [44]YU J,QIU M,JIANG J,et al.Modelling domain relationships for transfer learning on retrieval-based question answering systems in e-commerce[C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining.2018:682-690. |
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