Toward Self-Learning End-to-End Dialog Systems

• URL: http://arxiv.org/abs/2201.06849v1
• Date: Tue, 18 Jan 2022 09:56:35 GMT
• Title: Toward Self-Learning End-to-End Dialog Systems
• Authors: Xiaoying Zhang, Baolin Peng, Jianfeng Gao, Helen Meng
• Abstract summary: We propose SL-Agent, a self-learning framework for building end-to-end dialog systems in changing environments. SL-Agent consists of a dialog model and a pre-trained reward model to judge the quality of a system response. Experiments show that SL-Agent can effectively adapt to new tasks using limited human corrections.
• Score: 107.65369860922392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end task-oriented dialog systems often suffer from out-of-distribution (OOD) inputs after being deployed in dynamic, changing, and open environments. In this work, we propose SL-Agent, a self-learning framework that combines supervised learning, reinforcement learning, and machine teaching for building end-to-end dialog systems in a more realistic changing environment setting. SL-Agent consists of a dialog model and a pre-trained reward model to judge the quality of a system response. SL-Agent enables dialog agents to automatically adapt to environments with user behavior changes by learning from human-bot interactions via reinforcement learning, with the incorporated pre-trained reward model. We validate SL-Agent in four different dialog domains. Experimental results show the effectiveness of SL-Agent for automatically adapting to changing environments using both automatic and human evaluations. Furthermore, experiments on a challenging domain extension setting demonstrate that SL-Agent can effectively adapt to new tasks using limited human corrections provided via machine teaching. We will release code, data, and pre-trained models for further research.

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