中国综合性科技类核心期刊(北大核心)

中国科学引文数据库来源期刊(CSCD)

美国《化学文摘》(CA)收录

美国《数学评论》(MR)收录

俄罗斯《文摘杂志》收录

Message Board

Respected readers, authors and reviewers, you can add comments to this page on any questions about the contribution, review, editing and publication of this journal. We will give you an answer as soon as possible. Thank you for your support!

Name
E-mail
Phone
Title
Content
Verification Code
Issue 5
Sep.  2020
Turn off MathJax
Article Contents
GUO Xiaozhe, PENG Dunlu, ZHANG Yatong, PENG Xuegui. GRS: A generative retrieval dialogue model for intelligent customer service in the field of e-commerce[J]. Journal of East China Normal University (Natural Sciences), 2020, (5): 156-166. doi: 10.3969/j.issn.1000-5641.202091010
Citation: GUO Xiaozhe, PENG Dunlu, ZHANG Yatong, PENG Xuegui. GRS: A generative retrieval dialogue model for intelligent customer service in the field of e-commerce[J]. Journal of East China Normal University (Natural Sciences), 2020, (5): 156-166. doi: 10.3969/j.issn.1000-5641.202091010

GRS: A generative retrieval dialogue model for intelligent customer service in the field of e-commerce

doi: 10.3969/j.issn.1000-5641.202091010
  • Received Date: 2020-08-12
    Available Online: 2020-09-24
  • Publish Date: 2020-09-24
  • There are generally two ways to realize most intelligent chat systems: ① based on retrieval and ② based on generation. The content and type of responses, however, are limited by the corpus chosen. The generative approach can obtain responses that are not in the corpus, rendering it more flexible; at the same time, it is also easy to produce errors or meaningless replies. In order to solve the aforementioned problems, a new model GRS (generative retrieval score) is proposed. This model can train the retrieval model and the generation model simultaneously. A scoring module is used to rank the results of the retrieval model and the generation model, and the responses with high scores are taken as the output of the overall dialogue system. As a result, GRS can combine the advantages of both dialogue systems and output a specific, diverse, and flexible response. An experiment on a real-world JingDong intelligent customer service dialogue dataset shows that the proposed model offers better outputs than existing retrieval and generation models.
  • loading
  • [1]
    KEARNS L I J M, P KORMANN D, SINGH S, et al. Cobot in LambdaMOO: A social statistics agent [C]// Proceedings of the Seventeenth National Conference on Artificial Intelligence. 2001: 36-41.
    [2]
    JI Z, LU Z, LI H. An information retrieval approach to short text conversation [EB/OL].(2014-08-29)[2020-07-01] https://arxiv.org/pdf/1408.6988.pdf.
    [3]
    SORDONI A, GALLEY M, AULI M, et al. A neural network approach to context-sensitive generation of conversational responses [C]// Proceeding of NAACL-HLT. 2015: 196-205.
    [4]
    SERBAN I V, SORDONI A, BENGIO Y. et al. Building end-to-end dialogue systems using generative hierarchical neural network models [C]// Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. 2016: 3776-3784.
    [5]
    HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735-1780.
    [6]
    CHO K, MERRIENBOER B V, GULCEHRE C, et al. Learning phrase representations using RNN encoder decoder for statistical machine translation[EB/OL]. (2014-09-03)[2020-07-01]. https://arxiv.org/pdf/1406.1078.pdf.
    [7]
    YAN R, SONG Y P, 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.
    [8]
    WU Y, WU W, XING C, et al. Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots [EB/OL]. (2016-12-06)[2020-07-01]. https://arxiv.org/pdf/1612.01627.pdf.
    [9]
    YANG L, QIU M, QU C, et al. Response ranking with deep matching networks and external knowledge in information-seeking conversation systems [EB/OL]. (2018-05-01)[2020-07-01]. https://arxiv.org/pdf/1805.00188.pdf.
    [10]
    LI X, MOU L L, YAN R, ZHANG M. StalemateBreaker: A proactive content introducing approach to automatic human-computer conversation[EB/OL]. (2016-04-15)[2020-07-01]. https://arxiv.org/pdf/1604.04358.pdf.
    [11]
    GAO J F, GALLEY M, LI L H. Neural approaches to conversational AI [EB/OL]. (2019-09-10)[2020-07-01]. https://arxiv.org/pdf/1809.08267.pdf.
    [12]
    RITTER A, CHERRY C, DOLAN W B. Data-driven response generation in social media[C]//EMNLP '11: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011: 583–593.
    [13]
    ZOPH B, KNIGHT K. Multi-source neural translation[EB/OL]. (2019-01-05)[2020-07-01]. https://arxiv.org/pdf/1601.00710.pdf.
    [14]
    SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks[EB/OL]. (2014-12-14)[2020-07-01]. https://arxiv.org/pdf/1409.3215.pdf.
    [15]
    VINYALS O, LE Q. A neural conversational model [EB/OL]. (2015-06-19)[2020-07-01]. https://arxiv.org/pdf/1506.05869.pdf.
    [16]
    SERBAN I V, SORDONI A, LOWE R, et al. A hierarchical latent variable encoder-decoder model for generating dialogues [EB/OL].(2016-06-14)[2020-07-01]. https://arxiv.org/pdf/1605.06069.pdf.
    [17]
    BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate [EB/OL]. (2016-05-19)[2020-07-01]. https://arxiv.org/pdf/1409.0473.pdf.
    [18]
    VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: 6000-6010.
    [19]
    DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[EB/OL]. (2019-05-24)[2020-07-01]. https://arxiv.org/pdf/1810.04805.pdf.
    [20]
    SABOUR S, FROSST N, HINTON G E. Dynamic routing between capsules[EB/OL]. (2017-11-01)[2020-07-01]. https://arxiv.org/pdf/1710.09829.pdf.
    [21]
    YANG M, ZHAO W, YE J, et al. Investigating capsule networks with dynamic routing for text classification[EB/OL]. (2018-09-03)[2020-07-01]. https://arxiv.org/pdf/1804.00538.pdf.
    [22]
    ZHANG N, DENG S, SUN Z, et al. Attention-based capsule networks with dynamic routing for relation extraction [EB/OL]. (2018-12-29)[2020-07-01]. https://arxiv.org/pdf/1812.11321.pdf.
    [23]
    CHEN M, LIU R X, SHEN L. The JDDC corpus: A large-scale multi-turn Chinese dialogue dataset for e-commerce customer service [EB/OL]. (2019-11-25)[2020-07-01]. https://arxiv.org/pdf/1911.09969v2.pdf.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(3)

    Article views (94) PDF downloads(9) Cited by()
    Proportional views

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return