Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue

TL;DR

This paper introduces a step-by-step reinforcement learning method that jointly optimizes understanding and generation in task-oriented dialogue systems, leading to improved performance and state-of-the-art results.

Contribution

It extends reinforcement learning to both dialogue understanding and generation tasks with token-level rewards, addressing sparse reward issues and improving overall system performance.

Findings

Achieves state-of-the-art results on MultiWOZ2.0, MultiWOZ2.1, and In-Car datasets.

Demonstrates superior few-shot learning capabilities in low-resource scenarios.

Effectively balances understanding and generation through step-by-step rewards.

Abstract

Reinforcement learning (RL) is a powerful approach to enhance task-oriented dialogue (TOD) systems. However, existing RL methods tend to mainly focus on generation tasks, such as dialogue policy learning (DPL) or response generation (RG), while neglecting dialogue state tracking (DST) for understanding. This narrow focus limits the systems to achieve globally optimal performance by overlooking the interdependence between understanding and generation. Additionally, RL methods face challenges with sparse and delayed rewards, which complicates training and optimization. To address these issues, we extend RL into both understanding and generation tasks by introducing step-by-step rewards throughout the token generation. The understanding reward increases as more slots are correctly filled in DST, while the generation reward grows with the accurate inclusion of user requests. Our approach provides a balanced optimization aligned with task completion. Experimental results demonstrate that our approach effectively enhances the performance of TOD systems and achieves new state-of-the-art results on three widely used datasets, including MultiWOZ2.0, MultiWOZ2.1, and In-Car. Our approach also shows superior few-shot ability in low-resource settings compared to current models.