Dialog Action-Aware Transformer for Dialog Policy Learning

• URL: http://arxiv.org/abs/2309.02240v1
• Date: Tue, 5 Sep 2023 13:47:25 GMT
• Title: Dialog Action-Aware Transformer for Dialog Policy Learning
• Authors: Huimin Wang, Wai-Chung Kwan, Kam-Fai Wong
• Abstract summary: We propose to make full use of the plain text knowledge from the pre-trained language model to accelerate the RL agent's learning speed. Specifically, we design a dialog action-aware transformer encoder (DaTrans) which integrates a new fine-tuning procedure named masked last action task. DaTrans is further optimized in an RL setting with ongoing interactions and evolves through exploration in the dialog action space toward maximizing long-term accumulated rewards.
• Score: 22.262659702998892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent works usually address Dialog policy learning DPL by training a reinforcement learning (RL) agent to determine the best dialog action. However, existing works on deep RL require a large volume of agent-user interactions to achieve acceptable performance. In this paper, we propose to make full use of the plain text knowledge from the pre-trained language model to accelerate the RL agent's learning speed. Specifically, we design a dialog action-aware transformer encoder (DaTrans), which integrates a new fine-tuning procedure named masked last action task to encourage DaTrans to be dialog-aware and distils action-specific features. Then, DaTrans is further optimized in an RL setting with ongoing interactions and evolves through exploration in the dialog action space toward maximizing long-term accumulated rewards. The effectiveness and efficiency of the proposed model are demonstrated with both simulator evaluation and human evaluation.

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