Subgoal Discovery for Hierarchical Dialogue Policy Learning

@article{Tang2018SubgoalDF,
  title={Subgoal Discovery for Hierarchical Dialogue Policy Learning},
  author={D. Tang and Xiujun Li and Jianfeng Gao and C. Wang and L. Li and T. Jebara},
  journal={ArXiv},
  year={2018},
  volume={abs/1804.07855}
}
  • D. Tang, Xiujun Li, +3 authors T. Jebara
  • Published 2018
  • Computer Science
  • ArXiv
    • Developing agents to engage in complex goal-oriented dialogues is challenging partly because the main learning signals are very sparse in long conversations. In this paper, we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning. First, given successful example dialogues, we propose the Subgoal Discovery Network (SDN) to divide a complex goal-oriented task into a set of simpler subgoals in an unsupervised fashion… CONTINUE READING
    View on ACL
    arxiv.org
    32 Citations
    Background Citations
    16
    Methods Citations
    11

    Supplemental Video

    17:35
    Video
    Subgoal Discovery for Hierarchical Dialogue Policy Learning
    ACL
    12 December 2018
    Explore Further
    Discover more papers related to the topics discussed in this paper

    Figures, Tables, and Topics from this paper

    Figures and Tables

    Explore Further: Topics Discussed in This Paper

    Paper Mentions

    News Article
    Compositional Deep Learning
    Medium US
    13 November 2019
    32 Citations
    Citation Type

    Citation Type

    Meta Dialogue Policy Learning
    • 1
    • PDF
    A hierarchical approach for efficient multi-intent dialogue policy learning
    Goal-Oriented Dialogue Policy Learning from Failures
    • 8
    • PDF
    Learning to Dialogue via Complex Hindsight Experience Replay
    • 3
    Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management
    • 1
    • PDF
    Transfer Learning based Task-oriented Dialogue Policy for Multiple Domains using Hierarchical Reinforcement Learning
    • PDF
    Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System
    • 3
    • PDF
    Structured Hierarchical Dialogue Policy with Graph Neural Networks
    • PDF
    Hierarchical Reinforcement Learning for Open-Domain Dialog
    • 11
    • PDF
    Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings
    • 2
    • PDF

    References

    SHOWING 1-10 OF 50 REFERENCES
    Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
    • 98
    • PDF
    Integrating planning for task-completion dialogue policy learning
    • 78
    • PDF
    End-to-End Reinforcement Learning of Dialogue Agents for Information Access
    • 219
    • PDF
    Efficient Exploration for Dialogue Policy Learning with BBQ Networks & Replay Buffer Spiking
    • 49
    • PDF
    A User Simulator for Task-Completion Dialogues
    • 79
    • PDF
    End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning
    • 33
    • PDF
    Distributed dialogue policies for multi-domain statistical dialogue management
    • 37
    • PDF
    Scaling up deep reinforcement learning for multi-domain dialogue systems
    • 40
    • PDF
    Iterative policy learning in end-to-end trainable task-oriented neural dialog models
    • Bing Liu, I. Lane
    • Computer Science
    • 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
    • 2017
    • 54
    • PDF
    Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
    • 434
    • PDF