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Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-Oriented Dialogue Systems
Yihao Feng · Shentao Yang · Shujian Zhang · Jianguo Zhang · Caiming Xiong · Mingyuan Zhou · Huan Wang

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in Deep Reinforcement Learning Workshop »
Event URL: https://openreview.net/forum?id=YaEgoWIxwvk »

When learning task-oriented dialogue (TOD) agents, one can naturally utilize reinforcement learning (RL) techniques to train dialogue strategies to achieve user-specific goals. Prior works mainly focus on adopting advanced RL techniques to train the TOD agents, while the design of the reward function is not well studied. This paper aims at answering the question of how to efficiently learn and leverage a reward function for training end-to-end TOD agents. Specifically, we introduce two generalized objectives for reward-function learning, inspired by the classical learning-to-rank literature. Further, we utilize the learned reward-function to guide the training of the end-to-end TOD agent. With the proposed techniques, we achieve competitive results on the end-to-end response-generation task on the Multiwoz 2.0 dataset.

Author Information

Yihao Feng (Salesforce Research)

Researcher from Salesforce Research

Shentao Yang (The University of Texas at Austin)
Shujian Zhang (UT Austin)
Jianguo Zhang (Salesforce AI Research)
Caiming Xiong (Salesforce Research)
Mingyuan Zhou (University of Texas at Austin)
Huan Wang (Salesforce Research)

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