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

Citation Author(s):
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong
Submitted by:
Yun-Nung Chen
Last updated:
22 April 2018 - 12:00pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Yun-Nung Chen
Paper Code:
HLT-P2.4

Abstract 

Abstract: 

This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts. Then, we incorporate the discriminator as another critic into the advantage actor-critic (A2C) framework, to encourage the dialogue agent to explore state-action within the regions where the agent takes actions similar to those of the experts. Experimental results in a movie-ticket booking domain show that the proposed Adversarial A2C can accelerate policy exploration efficiently.

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Poster for Advantage A2C Dialogue Policy Learning

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