具有对立主体意识的学习目标导向对话策略
Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness
摘要：
大多数现有的面向目标的对话策略学习方法使用强化学习，它关注目标代理策略，并简单地将对方代理策略视为环境的一部分。而在现实世界的场景中，一个相对的代理的行为往往表现出某些模式或隐藏的政策，这可以推断和利用的目标代理，以促进自己的决策。这一策略在人类心理模拟中很常见，即在真正行动之前先想象一个特定的行动和可能的结果。因此，我们提出了一个面向目标的对话中策略学习的相反行为感知框架。我们估计对方代理的政策，从它的行为，并使用此估计，以改善目标代理作为目标政策的一部分。我们在合作和竞争对话任务上评估了我们的模型，显示出比最先进的基线更优越的性能。
英文摘要：
Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treat the opposite agent policy as part of the environment. While in real-world scenarios, the behavior of an opposite agent often exhibits certain patterns or underlies hidden policies, which can be inferred and utilized by the target agent to facilitate its own decision making. This strategy is common in human mental simulation by first imaging a specific action and the probable results before really acting it. We therefore propose an opposite behavior aware framework for policy learning in goal-oriented dialogues. We estimate the opposite agent's policy from its behavior and use this estimation to improve the target agent by regarding it as part of the target policy. We evaluate our model on both cooperative and competitive dialogue tasks, showing superior performance over state-of-the-art baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
