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Model discrepancy policy optimization for task-oriented dialogue
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AI
DOI：10.1016/j.csl.2024.101636.png)
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Abstract
En 中文
Task -oriented dialogue systems use deep reinforcement learning (DRL) to learn policies, and agent interaction with user models can help the agent enhance its generalization capacity. But user models frequently lack the language complexity of human interlocutors and contain generative errors, and their design biases can impair the agent's ability to function well in certain situations. In this paper, we incorporate an evaluator based on inverse reinforcement learning into the model to determine the quality of the dialogue of user models in order to recruit high -quality user models for training. We can successfully regulate the quality of training trajectories while maintaining their diversity by constructing a sampling environment distribution to pick high -quality user models to participate in policy learning. The evaluation on the Multiwoz dataset demonstrates that it is capable of successfully improving the dialogue agents' performance.
Keywords:
Task-oriented dialogue
Dialogue policy
Deep reinforcement learning
Inverse reinforcement learning
Journal
C
IF:
3.4
Papers:
1.5K
Citations:
2.6K