Communication is a cooperative effort that requires reaching mutual understanding among the participants. Humans use commonsense reasoning implicitly to produce natural and logically-coherent responses. As a step towards fluid human-AI communication, we study if response generation (RG) models can emulate human reasoning process and use common sense to help produce better-quality responses. We aim to tackle two research questions: how to formalize conversational common sense and how to examine RG models capability to use common sense? We first propose a task, CEDAR: Causal common sEnse in DiAlogue Response generation, that concretizes common sense as textual explanations for what might lead to the response and evaluates RG models behavior by comparing the modeling loss given a valid explanation with an invalid one. Then we introduce a process that automatically generates such explanations and ask humans to verify them. Finally, we design two probing settings for RG models targeting two reasoning capabilities using verified explanations. We find that RG models have a hard time determining the logical validity of explanations but can identify grammatical naturalness of the explanation easily.

该研究旨在通过探究响应生成模型对常识推理的理解程度以诱发适当的响应来缩小当前响应生成模型和人类沟通能力之间的差距，探测 RG 模型的常识推理能力并提出了两个探针设置来评估 RG 模型的常识推理功能。结果表明，模型无法捕获常识解释和响应之间的逻辑关系，领域内数据的微调和增加模型规模并不能带来对 CSR 的理解，这一研究有望激励更多的研究使 RG 模型效仿人类推理过程以实现流畅的人工智能交流。

对话回复生成中的常识解释探究