DeepAI AI Chat
Log In Sign Up

Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness

04/21/2020
by   Zheng Zhang, et al.
National University of Singapore
100tal.com
Tsinghua University
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

09/21/2020

Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems

Dialogue policy learning for task-oriented dialogue systems has enjoyed ...
01/29/2022

Explaining Reinforcement Learning Policies through Counterfactual Trajectories

In order for humans to confidently decide where to employ RL agents for ...
04/20/2018

Subgoal Discovery for Hierarchical Dialogue Policy Learning

Developing conversational agents to engage in complex dialogues is chall...
08/30/2019

Modeling Multi-Action Policy for Task-Oriented Dialogues

Dialogue management (DM) plays a key role in the quality of the interact...
04/07/2022

Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy

Proactive dialogue system is able to lead the conversation to a goal top...
03/08/2022

Policy Regularization for Legible Behavior

In Reinforcement Learning interpretability generally means to provide in...