基于强化学习的任务导向对话系统

Meina Song, Zhongfu Chen, Peiqing Niu, E. Haihong
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引用次数: 1

摘要

在本文中,我们提出了一个基于强化学习的面向任务的对话系统。整个系统由自然语言理解(NLU)、对话管理(DM)和自然语言生成(NLG)三部分组成。该模型能够与数据库进行实时交互,并从中获取有效信息。DM部分采用了强化学习。特别地,我们采用了改进的双深度q学习(DQN)策略。在这种情况下,DM剂可以很好地抵抗环境噪声。此外,我们还提出了一种NLU模块的联合模型,并在ATIS和Snips数据集上进行了实验,验证了该联合模型的有效性。对于整个系统,实验是在一个公共电影票预订数据集上进行的。实验结果表明,该模型在模拟用户和真实用户上都优于传统的基于规则的多回合对话系统。此外,双dqn智能体在客观评价和主体评价方面都有更好的表现,证明了我们所提出模型的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Task-oriented Dialogue System Based on Reinforcement Learning
In this paper, we propose a task-oriented dialogue system based on reinforcement learning. The overall system is composed of three parts: natural language understanding (NLU), dialogue management (DM) and natural language generation (NLG). And our model can interact with the database in real time and acquire effective information from it. For the DM part, reinforcement learning is applied. Specially, we adopt an improved double deep Q-learning (DQN) strategy. In that case, the DM agent can resist the environmental noise considerably. Besides, we put forward a joint model for NLU module, and the experiments on ATIS and Snips datasets have proved the effectiveness of the joint model. For the overall system, the experiments are conducted on a public movie-ticket booking dataset. The experimental results indicate that the proposed model has outperformed the traditional rule-based multi-turn dialogue system both on simulated and real users. Besides, the double-DQN agent has better performance for both objective and subject evaluation, which demonstrates the effectiveness and superiority of our proposed model.
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