ISSN 0253-2778

CN 34-1054/N

open
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Open AccessOpen Access JUSTC Original Paper

Dialogue matching prediction model applied in campus psychological counseling

  • School of Computer Science and Technology, USTC, Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.09.008
  • Received Date: 09 March 2018
  • Accepted Date: 18 May 2018
  • Rev Recd Date: 18 May 2018
  • Publish Date: 30 September 2018
    Abstract

  • Abstract

    Chat-bots have received wide attention in both academia and industry.In academia,there have been many promising research results in the end-to-end dialogue response area.Among them,data-driven dialogue response methods predominate,which learn and understand natural language through deep neural networks.Existing dialogue response models are mainly designed for open domains.The current mature chat-bot applications are mostly used for entertainment.Methods used on professional chat-bots (like psychological counseling chat-bots) are mainly based on rule and template.To enhance the intelligence of the psychological counseling chat-bot, a new method of modeling dialogue matching pattern in the context of campus counseling is proposed.This method is based on the psychological counseling website and Tieba corpus,from which relevant characteristics of words and sentences in the category of psychological counseling types are extracted,and are applied to machine learning and deep learning networks to model the dialogue matching pattern.Compared with traditional dialogue matching models in open domain,the proposed model achieved better matching results with the use of analyzed psychological counseling information.

    Abstract

    Chat-bots have received wide attention in both academia and industry.In academia,there have been many promising research results in the end-to-end dialogue response area.Among them,data-driven dialogue response methods predominate,which learn and understand natural language through deep neural networks.Existing dialogue response models are mainly designed for open domains.The current mature chat-bot applications are mostly used for entertainment.Methods used on professional chat-bots (like psychological counseling chat-bots) are mainly based on rule and template.To enhance the intelligence of the psychological counseling chat-bot, a new method of modeling dialogue matching pattern in the context of campus counseling is proposed.This method is based on the psychological counseling website and Tieba corpus,from which relevant characteristics of words and sentences in the category of psychological counseling types are extracted,and are applied to machine learning and deep learning networks to model the dialogue matching pattern.Compared with traditional dialogue matching models in open domain,the proposed model achieved better matching results with the use of analyzed psychological counseling information.
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    • References(23)
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    [1]
    黄红,张佩珍.大学生心理行为指导[M].上海:上海大学出版社, 2003.
    [2]
    张伟男,刘挺.聊天机器人技术的研究进展[J].中国人工智能学会通讯,2016,1: 17-21.
    [3]
    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.
    [4]
    GANGADHARAIAH R, NARAYANASWAMY B M, ELKAN C. Achieving fluency and coherency in task-oriented dialog[J]. Artificial Intelligence, 2017, arXiv:1804.03799.
    [5]
    LI J, GALLEY M, BROCKETT C, et al. A diversity-promoting objective function for neural conversation models[J]. OALib Journal, 2015, arXiv:1510.03055.
    [6]
    MOU L, SONG Y, YAN R, et al. Sequence to backward and forward sequences: A content-introducing approach to generative short-text conversation[J]. Machine Learning, 2016, arXiv:1607.00970.
    [7]
    XING C, WU W, WU Y, et al. Topic aware neural response generation[J]. 2016, arXiv:1606.08340.
    [8]
    ZHOU H, HUANG M, ZHANG T, et al. Emotional chatting machine: Emotional conversation generation with internal and external memory[J]. 2017, arXiv:1704.01074.
    [9]
    LI J, GALLEY M, BROCKETT C, et al. A persona-based neural conversation model[J]. 2016, arXiv:1603.06155.
    [10]
    LI J, MILLER A H, CHOPRA S, et al. Learning through dialogue interactions[J]. 2016, arXiv:1612.04936.
    [11]
    LI X, MOU L, YAN R, et al. StalemateBreaker: A proactive content-introducing approach to automatic human-computer conversation[J]. 2016, arXiv:1604.04358.
    [12]
    ZHOU H, HUANG M, ZHANG T, et al. Emotional chatting machine: emotional conversation generation with internal and external memory[J]. 2017, arXiv:1704.01074.
    [13]
    ASGHAR N, POUPART P, HOEY J, et al. Affective neural response generation[J]. 2017, arXiv:1709.03968.
    [14]
    LOWE R, POW N, SERBAN I, et al. The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems[J]. 2015, arXiv:1506.08909.
    [15]
    LU Z, LI H. A deep architecture for matching short texts[C]//Advances in Neural Information Processing Systems. Lake Tahoe, USA: NIPS Press, 2013: 1367-1375.
    [16]
    HU B, LU Z, LI H, et al. Convolutional neural network architectures for matching natural language sentences[C]//Advances in neural information processing systems. Montreal, Canada: NIPS Press, 2014: 2042-2050.
    [17]
    ZHOU X, DONG D, WU H, et al. Multi-view response selection for human-computer conversation[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Austin, USA: IEEE Press, 2016: 372-381.
    [18]
    WU Y, WU W, LI Z, et al. Topic augmented neural network for short text conversation[J]. 2016, CoRR abs/1605.00090.
    [19]
    WU Y, WU W, XING C, et al. Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.Vancouver, Canada: IEEE Press, 2017, 1: 496-505.
    [20]
    ELMASRI D, MAEDER A. A conversational agent for an online mental health intervention[C]//International Conference on Brain and Health Informatics. Springer, 2016: 243-251.
    [21]
    CHITTARANJAN G, BLOM J, GATICA-PEREZ D. Mining large-scale smartphone data for personality studies[J]. Personal and Ubiquitous Computing, 2013, 17(3): 433-450.
    [22]
    WEI H, ZHANG F, YUAN N J, et al. Beyond the words: Predicting user personality from heterogeneous information[C]//Proceedings of the 10th ACM international Conference on Web Search and Data Mining. Cambridge, UK: ACM Press, 2017: 305-314.
    [23]
    GUO A, MA J. Archetype-based modeling of persona for comprehensive personality computing from personal big data[J]. Sensors, 2018, 18(3): 684.)
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