利用显式推理进行常识增强对话模型中的推理集成
Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models
AI总结：
本研究提出在开放域对话中采用显式推理步骤生成、选择并集成常识，相比隐式推理显著提升回复的自然度、参与度、具体性和整体质量，达到新最优性能。
摘要：
开放域对话系统需要掌握社会常识，以便有效理解和回应用户。已有研究提出了常识增强的对话模型，旨在从对话上下文中推断常识知识，以提高回复质量。然而，现有的常识增强对话方法在生成回复时依赖隐式推理来集成常识推断。在本研究中，我们探讨了显式推理相对于隐式推理对对话回复生成中常识处理的影响。我们的研究结果表明，将常识推理分离为显式步骤，用于生成、选择常识并将其集成到回复中，能够带来更好的对话交互，提升自然度、参与度、具体性和整体质量。对上述结果的后续分析揭示了不同类型的常识在生成回复中的有效性，以及通过显式推理进行常识集成所增强的特定回复特征。我们的工作通过实现常识增强回复生成的新最优性能，推进了开放域对话的研究。
英文摘要：
Open-domain dialogue systems need to grasp social commonsense to understand and respond effectively to human users. Commonsense-augmented dialogue models have been proposed that aim to infer commonsense knowledge from dialogue contexts in order to improve response quality. However, existing approaches to commonsense-augmented dialogue rely on implicit reasoning to integrate commonsense inferences during response generation. In this study, we explore the impact of explicit reasoning against implicit reasoning over commonsense for dialogue response generation. Our findings demonstrate that separating commonsense reasoning into explicit steps for generating, selecting, and integrating commonsense into responses leads to better dialogue interactions, improving naturalness, engagement, specificity, and overall quality. Subsequent analyses of these findings unveil insights into the effectiveness of various types of commonsense in generating responses and the particular response traits enhanced through explicit reasoning for commonsense integration. Our work advances research in open-domain dialogue by achieving a new state-of-the-art in commonsense-augmented response generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
