Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning

@inproceedings{Peng2017CompositeTD,
  title={Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning},
  author={Baolin Peng and Xiujun Li and Lihong Li and Jianfeng Gao and Asli Çelikyilmaz and Sungjin Lee and Kam-Fai Wong},
  booktitle={EMNLP},
  year={2017}
}
Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep… CONTINUE READING
Highly Cited
This paper has 53 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 30 extracted citations

Computational Science – ICCS 2018

View 10 Excerpts
Highly Influenced

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2018

53 Citations

0204060201720182019
Citations per Year
Semantic Scholar estimates that this publication has 53 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 32 references

Policy committee for adaptation in multi-domain spoken dialogue systems

2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) • 2015
View 4 Excerpts
Highly Influenced

Scaling up deep reinforcement learning for multi-domain dialogue systems

2017 International Joint Conference on Neural Networks (IJCNN) • 2017
View 3 Excerpts

Similar Papers

Loading similar papers…