结合情感信息的个性化对话生成

doi:10.11896/jsjkx.211100019

摘要/Abstract

摘要： 如今,人机对话系统受到了越来越多的关注,但目前主流的人机对话系统很少考虑说话者的个性化特征。对话系统的一个重要且有待探索的方面是根据交互人员的个性来提升对话的响应质量。个性化是创建智能对话系统的关键,可以最大程度地适应到人类的生活中。然而,在自然语言处理中体现人物个性是很困难的,在个性化对话生成中,情感也是一个很重要的因素,因此文中提出了融合属性级情感的个性化对话生成模型。该模型使用BERT-MRC模型抽取人物个性和历史对话的情感词属性词信息,采用改进的UNILM神经网络模型对人物个性以及历史对话进行编码,同时在编码表征时结合情感词信息和属性词信息,最终生成符合人物个性的对话。实验证明,结合情感信息的个性化对话生成方法能够有效地提升个性化对话生成的质量,增加生成回复的多样性。

关键词: 自然语言处理, 对话生成, 个性化, 神经网络, 情感, 属性

Abstract: Nowadays,more and more attention has been paid to the man-machine dialogue system.However,the current mainstream man-machine dialogue system rarely considers the personalized characteristics of the speaker.An important aspect of the dialogue system is to improve the response quality of dialogue according to the personality of interactive personnel.Personalization is the key to create intelligent dialogue system,which can be well adapted to human life.Emotion is a very important factor in the generation of personalized dialogue.Therefore,a personalized dialogue generation model integrating attribute level emotion is proposed in this paper.The BERT-MRC model is used to extract the emotional and attribute information of character personality and historical dialogue.The improved UNILM neural network model is used to encode character personality and historical dialogue.At the same time,the emotional word information and attribute word information are combined in the coding representation to finally generate a dialogue in line with character personality.Experiments show that the proposed method can effectively improve the quality of personalized dialogue generation and increase the diversity of generated responses.

Key words: Natural language processing, Dialogue generation, Personality, Neural network, Emotion, Attribute

中图分类号:

TP183

徐晖, 王中卿, 李寿山, 张民. 结合情感信息的个性化对话生成[J]. 计算机科学, 2022, 49(11A): 211100019-6. https://doi.org/10.11896/jsjkx.211100019

XU Hui, WANG Zhong-qing, LI Shou-shan, ZHANG Min. Personalized Dialogue Generation Integrating Sentimental Information[J]. Computer Science, 2022, 49(11A): 211100019-6. https://doi.org/10.11896/jsjkx.211100019

参考文献

[1]BROWN P F,PIETRA V J D,PIETRA S A D,et al.The mathematics of statistical machine translation:Parameter estimation[J].Computational Linguistics,1993,19(2):263-311.
[2]SUTSKEVER I,VINYALS O,LE Q V.Sequence to sequencelearning with neural networks[C]//Advances in Neural Information Processing Systems.2014:3104-3112.
[3]CHO K,VAN MERRIËNBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[J].arXiv:1406.1078,2014.
[4]BENGIO Y,DUCHARME R,VINCENT P,et al.A neuralprobabilistic language model[J].Journal of Machine Learning Research,2003,3(2):1137-1155.
[5]MIKOLOV T,KARAFIÁT M,BURGET L,et al.Recurrent neural network based language model[C]//INTERSPEECH 2010,Conference of the International Speech Communication Association.DBLP,2010:1045-1048.
[6]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[7]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Advances in Neural Information Processing Systems.2017:5998-6008.
[8]MCKAY B D.Practical graph isomorphism[M].Tennessee,USA:Department of Computer Science,Vanderbilt University,1981:45-87.
[9]SERBAN I V,SORDONI A,BENGIO Y,et al.Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models[C]//AAAI.2016:3776-3784.[10]ZHAO Y Y,QIN B,LIU T.Sentiment analysis[J].Journal of Software,2010,21(8):1834-1848.
[11]DONG L,YANG N,WANG W,et al.Unified language model pre-training for natural language understanding and generation[C]//Proceedings of the Advances in Neural Information Processing Systems.Vancouver,2019:13042-13054.
[12]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.2018:2204-2213.
[13]CHEN H,LIU X,YIN D,et al.A survey on dialogue systems:Recent advances and new frontiers[J].ACM SIGKDD Explorations Newsletter,2017,19(2):25-35.
[14]SORDONI A,GALLEY M,AULI M,et al.A Neural Network Approach to Context-Sensitive Generation of Conversational Responses[J].Transactions of the Royal Society of Tropical Medicine & Hygiene,2015,51(6):502-504.
[15]VINYALS O,LE Q.A neural conversational model[J].arXiv:1506.05869,2015.
[16]LI J W,GALLEY M,BROCKETT C,et al.A Diversity-Promoting Objective Function for Neural Conversation Models[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Association for Computational Linguistics,2016:110-119.
[17]SERBAN I V,SORDONI A,BENGIO Y,et al.Building end-to-end dialogue systems using generative hierarchical neural network models[C]//Thirtieth AAAI Conference on Artificial Intelligence.New York:CAM Press,2016:3776-3883.
[18]SHANG L F,LU Z D,LI H.Neural Responding Machine forShort-Text Conversation[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.Association for Computational Linguistics,2015:1577-1586.
[19]RADFORD A,NARASIMHAN K,SALIMANS T,et al.Improving language understanding bygenerative pre-training[J/OL].https://s3-us-west-2.amazonaws.com/openai-assets/ researchcovers/languageunsupervised/language understandingpaper.pdf,2018.
[20]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018.
[21]LUO L,HUANG W,QI Z,et al.Learning Personalized End-to-End Goal-Oriented Dialog[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:6794-6801.
[22]LI J,GALLEY M,BROCKETT C,et al.A personabased neural conversation model [C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.2016:994-1003.
[23]WANG J,WANG X,LI F,et al.Group linguistic bias awareneural response generation[C]//Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing.2017:1-10.
[24]LIU B,XU Z,SUN C,et al.Content-oriented user modeling for personalized response ranking in chatbots[J].IEEE/ACM Transactions on Audio,Speech and Language Processing(TASLP),2018,26(1):122-133.
[25]LUO L,HUANG W,QI Z,et al.Learning Personalized End-to-End Goal-Oriented Dialog.Proceedings of the AAAI Conference on Artificial Intelligence.2019:6794-6801.
[26]ZHENG Y,CHEN G,HUANG M,et al.Personalized dialogue generation with diversified traits[J].arXiv:/1901.09672,2019.
[27]LIN Z,MADOTTO A,WU C S,et al.Personalizing dialogue agents via meta-learning[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.2019:5454-5459.
[28]ZHENG Y.A Pre-Training Based Personalized Dialogue Generation Model with Persona-Sparse Data[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:9693-9700.
[29]ZHANG Y,SUN S,GALLEY M,et al.DialoGPT:Large-Scale Generative Pre-training for Conversational Response Generation[J].2019.

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