2023/11/14 by Do June Min, Min, Do June, Paloma Sodhi +3 · 1 citation
Computer Science · #AI in Service Interactions #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Speech and dialogue systems #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2311.08300
openalex publication_date 2023/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Task-oriented dialogue (TOD) systems aim to achieve specific goals through interactive dialogue. Such tasks usually involve following specific workflows, i.e. executing a sequence of actions in a particular order. While prior work has focused on supervised learning methods to condition on past actions, they do not explicitly optimize for compliance to a desired workflow. In this paper, we propose a novel framework based on reinforcement learning (RL) to generate dialogue responses that are aligned with a given workflow. Our framework consists of ComplianceScorer, a metric designed to evaluate how well a generated response executes the specified action, combined with an RL opimization process that utilizes an interactive sampling technique. We evaluate our approach on two TOD datasets, Action-Based Conversations Dataset (ABCD) (Chen et al., 2021a) and MultiWOZ 2.2 (Zang et al., 2020) on a range of automated and human evaluation metrics. Our findings indicate that our RL-based framework outperforms baselines and is effective at enerating responses that both comply with the intended workflows while being expressed in a natural and fluent manner.