Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2022, Vol. 62 Issue (5): 952-958    DOI: 10.16511/j.cnki.qhdxxb.2021.21.037
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
基于场景与话题的聊天型人机会话系统
陆思聪, 李春文
清华大学 自动化系, 北京 100084
Human-machine conversation system for chatting based on scene and topic
LU Sicong, LI Chunwen
Department of Automation, Tsinghua University, Beijing 100084, China
全文: PDF(1216 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 人机会话在自然语言处理乃至人工智能领域中起着重要标志性作用,可根据使用目的划分为问答系统、任务型会话、聊天系统等,其中聊天型会话通常具有更高的拟人需求。该文在基于长短期记忆网络的序列变换模型基础上,通过引入话题网络显式抽取会话中的场景与话题信息,并将这种不随语序变化的高层抽象信息扩展到会话模型结构中,与注意力机制共同指导解码预测过程。由于难以事先获取话题信息,话题网络被建模为非监督式学习模型,因此训练过程需分三步进行。实验结果表明,在恰当的训练方法和结构参数下,该模型能够使聊天会话的质量得到明显提升。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
陆思聪
李春文
关键词 人工智能自然语言处理人机会话机器学习话题网络    
Abstract:Human-machine conversation plays an important role in natural language processing and artificial intelligence. Human-machine conversation can be divided into the question answering system, task-oriented conversation, and chatting system according to the purpose of use. Among them, the chatting system usually requires higher personification. Based on the sequence transformation model of the long short-term memory network, the topic network is introduced in this study to explicitly extract the scene and topic information from the conversation, and this higher-level feature, which does not change with the word order, is inputted to the structure of the conversation model to guide the decoding and prediction processes together with the attention mechanism. Because of the difficulty of obtaining the topic information in advance, the topic network is modeled as an unsupervised learning structure. Thus, the training process needs to be divided into three steps. The experimental results show that the model can significantly improve the quality of the chatting system with appropriate training methods and structural parameters.
Key wordsartificial intelligence    natural language processing    human-machine conversation    machine learning    topic network
收稿日期: 2021-05-11      出版日期: 2022-04-26
基金资助:国家自然科学基金面上项目(61174068)
通讯作者: 李春文,教授,E-mail:lcw@tsinghua.edu.cn      E-mail: lcw@tsinghua.edu.cn
作者简介: 陆思聪(1989—),男,博士研究生。
引用本文:   
陆思聪, 李春文. 基于场景与话题的聊天型人机会话系统[J]. 清华大学学报(自然科学版), 2022, 62(5): 952-958.
LU Sicong, LI Chunwen. Human-machine conversation system for chatting based on scene and topic. Journal of Tsinghua University(Science and Technology), 2022, 62(5): 952-958.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2021.21.037  或          http://jst.tsinghuajournals.com/CN/Y2022/V62/I5/952
  
  
  
  
  
  
  
  
[1] SHAWAR B A, ATWELL E S. Using corpora in machine-learning chatbot systems[J]. International Journal of Corpus Linguistics, 2005, 10(4):489-516.
[2] 易顺明, 胡振宇. 中文聊天机器人原型系统的设计[J]. 沙洲职业工学院学报, 2007, 10(2):5-9. YI S M, HU Z Y. The prototype design for the Chinese chat robots[J]. Journal of Shazhou Professional Institute of Technology, 2007, 10(2):5-9. (in Chinese)
[3] SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks[EB/OL]. (2014-12-14)[2021-05-01]. https://arxiv.org/abs/1409.3215v3.
[4] SUNDERMEYER M, NEY H, SCHLVTER R. From feedforward to recurrent LSTM neural networks for language modeling[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2015, 23(3):517-529.
[5] LUONG M T, PHAM H, MANNING C D. Effective approaches to attention-based neural machine translation[EB/OL]. (2015-09-20)[2021-05-01]. https://arxiv.org/abs/1508.04025v5.
[6] 王红, 史金钏, 张志伟. 基于注意力机制的LSTM的语义关系抽取[J]. 计算机应用研究, 2018, 35(5):1417-1420, 1440. WANG H, SHI J C, ZHANG Z W. Text semantic relation extraction of LSTM based on attention mechanism[J]. Application Research of Computers, 2018, 35(5):1417-1420, 1440. (in Chinese)
[7] NIO L, SAKTI S, NEUBIG G, et al. Conversation dialog corpora from television and movie scripts[C]//2014 17th Oriental Chapter of the International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques (COCOSDA). Phuket, Thailand:IEEE Press, 2014:1-4.
[8] 曾小芹. 基于Python的中文结巴分词技术实现[J]. 信息与电脑(理论版), 2019, 31(18):38-39, 42. ZENG X Q. Technology implementation of Chinese jieba segmentation based on python[J]. China Computer & Communication, 2019, 31(18):38-39, 42. (in Chinese)
[9] RONG X. Word2vec parameter learning explained[EB/OL]. (2016-06-05)[2021-05-01]. https://arxiv.org/abs/1411.2738v4.
[10] 张伟男, 张杨子, 刘挺. 对话系统评价方法综述[J]. 中国科学:信息科学, 2017, 47(8):953-966. ZHANG W N, ZHANG Y Z, LIU T. Survey of evaluation methods for dialogue systems[J]. Scientia Sinica (Informationis), 2017, 47(8):953-966. (in Chinese)
[11] MOLDOVAN D I, TATU M. Natural language question answering system and method utilizing multi-modal logic:US20060053000 A1[P]. 2006-03-09.
[12] 邢超. 智能问答系统的设计与实现[D]. 北京:北京交通大学, 2015. XING C. The design and implementation of intelligent question and answering system[D]. Beijing:Beijing Jiaotong University, 2015. (in Chinese)
[13] WEN T H, VANDYKE D, MRKSIC N, et al. A network-based end-to-end trainable task-oriented dialogue system[EB/OL]. (2017-04-24)[2021-05-01]. https://arxiv.org/abs/1604.04562v3.
[14] 张杰晖. 任务型对话系统的自然语言生成研究[D]. 广州:华南理工大学, 2019. ZHANG J H. Research on natural language generation in task-based dialogue system[D]. Guangzhou:South China University of Technology, 2019. (in Chinese)
[15] MĚKOTA O, GÖKIRMAK M, LAITOCH P. End to end dialogue transformer[EB/OL]. (2020-08-24)[2021-05-01]. https://www.researchgate.net/publication/343849046_End _to_End_Dialogue_Transformer.
[16] THIERGART J, HUBER S, VBELLACKER T. Under-standing emails and drafting responses-An approach using GPT-3[EB/OL]. (2021-02-15)[2021-05-01]. https://arxiv.org/abs/2102.03062v3.
[17] 张献涛, 张猛, 暴筱, 等. 一种提升人机交互对话语料质量与多样性的对话语料库生成方法:CN111026884A[P]. 2020-04-17. ZHANG X T, ZHANG M, BAO X, et al. Dialogue corpus generation method for improving quality and diversity of man-machine interaction dialogue corpora:CN111026884A[P]. 2020-04-17. (in Chinese)
[18] WIKIPEDIA. Long short-term memory[EB/OL]. (2021-03-25)[2021-05-01]. https://en.wikipedia.org/wiki/Long_short-term_memory.
[19] WIKIPEDIA. Attention (machine learning)[EB/OL]. (2021-02-27)[2021-05-01]. https://en.wikipedia.org/wiki/Attention_(machine_learning).
[20] WIKIPEDIA. BLEU[EB/OL]. (2020-11-09)[2021-05-01]. https://en.wikipedia.org/wiki/BLEU.
[21] WIKIPEDIA. Autoencoder[EB/OL]. (2021-03-24)[2021-05-01]. https://en.wikipedia.org/wiki/Autoencoder.
[22] DANESCU-NICULESCU-MIZIL C, LEE L. Chameleons in imagined conversations:A new approach to understanding coordination of linguistic style in dialogs[C]//CMCL 2011:Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics. Portland, Oregon, USA:Association for Computational Linguistics, 2011:76-87.
[23] GITHUB. Fxsjy/jieba[DB/OL]. (2020-01-20)[2021-05-01]. https://github.com/fxsjy/jieba.
[24] WIKIPEDIA. Beam search[EB/OL]. (2021-03-11)[2021-05-01]. https://en.wikipedia.org/wiki/Beam_search.
[1] 王庆人, 王银子, 仲红, 张以文. 面向中文的字词组合序列实体识别方法[J]. 清华大学学报(自然科学版), 2023, 63(9): 1326-1338.
[2] 吴浩, 牛风雷. 高温球床辐射传热中的机器学习模型[J]. 清华大学学报(自然科学版), 2023, 63(8): 1213-1218.
[3] 代鑫, 黄弘, 汲欣愉, 王巍. 基于机器学习的城市暴雨内涝时空快速预测模型[J]. 清华大学学报(自然科学版), 2023, 63(6): 865-873.
[4] 任建强, 崔亚鹏, 倪顺江. 基于机器学习的新冠肺炎疫情趋势预测方法[J]. 清华大学学报(自然科学版), 2023, 63(6): 1003-1011.
[5] 安健, 陈宇轩, 苏星宇, 周华, 任祝寅. 机器学习在湍流燃烧及发动机中的应用与展望[J]. 清华大学学报(自然科学版), 2023, 63(4): 462-472.
[6] 赵祺铭, 毕可鑫, 邱彤. 基于机器学习的乙烯裂解过程模型比较与集成[J]. 清华大学学报(自然科学版), 2022, 62(9): 1450-1457.
[7] 李庆斌, 马睿, 胡昱, 皇甫泽华, 沈益源, 周绍武, 马金刚, 安再展, 郭光文. 大坝智能建造研究进展与发展趋势[J]. 清华大学学报(自然科学版), 2022, 62(8): 1252-1269.
[8] 刘天云. 大型填筑工程3D打印技术与应用[J]. 清华大学学报(自然科学版), 2022, 62(8): 1281-1291.
[9] 曹来成, 李运涛, 吴蓉, 郭显, 冯涛. 多密钥隐私保护决策树评估方案[J]. 清华大学学报(自然科学版), 2022, 62(5): 862-870.
[10] 王豪杰, 马子轩, 郑立言, 王元炜, 王飞, 翟季冬. 面向新一代神威超级计算机的高效内存分配器[J]. 清华大学学报(自然科学版), 2022, 62(5): 943-951.
[11] 李瑞敏, 王长君. 智能交通管理系统发展趋势[J]. 清华大学学报(自然科学版), 2022, 62(3): 509-515.
[12] 李维, 李城龙, 杨家海. As-Stream:一种针对波动数据流的算子智能并行化策略[J]. 清华大学学报(自然科学版), 2022, 62(12): 1851-1863.
[13] 刘强墨, 何旭, 周佰顺, 吴昊霖, 张弛, 秦羽, 沈晓梅, 高小榕. 基于机器学习和瞳孔响应的简易高性能自闭症分类模型[J]. 清华大学学报(自然科学版), 2022, 62(10): 1730-1738.
[14] 马晓悦, 孟啸. 用户参与视角下多图推文的图像位置和布局效应[J]. 清华大学学报(自然科学版), 2022, 62(1): 77-87.
[15] 胡滨, 耿天玉, 邓赓, 段磊. 基于知识蒸馏的高效生物医学命名实体识别模型[J]. 清华大学学报(自然科学版), 2021, 61(9): 936-942.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn