GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning

@article{Liu2020GoChatGC,
  title={GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning},
  author={Jianfeng Liu and Feiyang Pan and Ling Luo},
  journal={Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2020}
}
  • Jianfeng Liu, Feiyang Pan, Ling Luo
  • Published 2020
  • Computer Science
  • Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
  • A chatbot that converses like a human should be goal-oriented (i.e., be purposeful in conversation), which is beyond language generation. However, existing goal-oriented dialogue systems often heavily rely on cumbersome hand-crafted rules or costly labelled datasets, which limits the applicability. In this paper, we propose Goal-oriented Chatbots (GoChat), a framework for end-to-end training the chatbot to maximize the long-term return from offline multi-turn dialogue datasets. Our framework… CONTINUE READING
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    References

    SHOWING 1-8 OF 8 REFERENCES
    ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation
    • 31
    • Highly Influential
    • PDF
    Hierarchical Recurrent Attention Network for Response Generation
    • 109
    • Highly Influential
    • PDF
    Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
    • 1,154
    • Highly Influential
    • PDF
    A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
    • 697
    • Highly Influential
    • PDF
    How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models
    • 79
    • Highly Influential
    • PDF
    Adam: A Method for Stochastic Optimization
    • 58,627
    • Highly Influential
    • PDF
    Asynchronous Methods for Deep Reinforcement Learning
    • 3,770
    • Highly Influential
    • PDF
    Hierarchical Attention Networks for Document Classification
    • 2,438
    • Highly Influential
    • PDF