Workflow-Guided Response Generation for Task-Oriented Dialogue

• URL: http://arxiv.org/abs/2311.08300v1
• Date: Tue, 14 Nov 2023 16:44:33 GMT
• Title: Workflow-Guided Response Generation for Task-Oriented Dialogue
• Authors: Do June Min and Paloma Sodhi and Ramya Ramakrishnan
• Abstract summary: 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. Our findings indicate that our RL-based framework outperforms baselines and is effective at enerating responses that both comply with the intended while being expressed in a natural and fluent manner.
• Score: 4.440232673676693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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