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    Explicit State Tracking with Semi-Supervisionfor Neural Dialogue Generation

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    Type
    Conference Paper
    Authors
    Jin, Xisen
    Lei, Wenqiang
    Ren, Zhaochun
    Chen, Hongshen
    Liang, Shangsong
    Zhao, Yihong
    Yin, Dawei
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-10-22
    Online Publication Date
    2018-10-22
    Print Publication Date
    2018
    Permanent link to this record
    http://hdl.handle.net/10754/630329
    
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    Abstract
    The task of dialogue generation aims to automatically provide responses given previous utterances. Tracking dialogue states is an important ingredient in dialogue generation for estimating users' intention. However, the expensive nature of state labeling and the weak interpretability make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states. In this paper, we propose the semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation. To this end, our approach has two core ingredients: CopyFlowNet and posterior regularization. Specifically, we propose an encoder-decoder architecture, named CopyFlowNet, to represent an explicit dialogue state with a probabilistic distribution over the vocabulary space. To optimize the training procedure, we apply a posterior regularization strategy to integrate indirect supervision. Extensive experiments conducted on both task-oriented and non-task-oriented dialogue corpora demonstrate the effectiveness of our proposed model. Moreover, we find that our proposed semi-supervised dialogue state tracker achieves a comparable performance as state-of-the-art supervised learning baselines in state tracking procedure.
    Citation
    Jin X, Lei W, Ren Z, Chen H, Liang S, et al. (2018) Explicit State Tracking with Semi-Supervisionfor Neural Dialogue Generation. Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM ’18. Available: http://dx.doi.org/10.1145/3269206.3271683.
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM '18
    Conference/Event name
    27th ACM International Conference on Information and Knowledge Management, CIKM 2018
    DOI
    10.1145/3269206.3271683
    arXiv
    1808.10596
    Additional Links
    https://dl.acm.org/citation.cfm?doid=3269206.3271683
    ae974a485f413a2113503eed53cd6c53
    10.1145/3269206.3271683
    Scopus Count
    Collections
    Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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