Reasoning, a crucial aspect of NLP research, has not been adequately addressed by prevailing models including Large Language Model. Conversation reasoning, as a critical component of it, remains largely unexplored due to the absence of a well-designed cognitive model. In this paper, inspired by intuition theory on conversation cognition, we develop a conversation cognitive model (CCM) that explains how each utterance receives and activates channels of information recursively. Besides, we algebraically transformed CCM into a structural causal model (SCM) under some mild assumptions, rendering it compatible with various causal discovery methods. We further propose a probabilistic implementation of the SCM for utterance-level relation reasoning. By leveraging variational inference, it explores substitutes for implicit causes, addresses the issue of their unobservability, and reconstructs the causal representations of utterances through the evidence lower bounds. Moreover, we constructed synthetic and simulated datasets incorporating implicit causes and complete cause labels, alleviating the current situation where all available datasets are implicit-causes-agnostic. Extensive experiments demonstrate that our proposed method significantly outperforms existing methods on synthetic, simulated, and real-world datasets. Finally, we analyze the performance of CCM under latent confounders and propose theoretical ideas for addressing this currently unresolved issue.

翻译：推理作为NLP研究的关键领域，尚未得到包括大语言模型在内的主流模型的充分解决。对话推理作为其重要组成部分，因缺乏设计完善的认知模型而仍基本未被探索。本文受对话认知直觉理论启发，构建了一个对话认知模型(CCM)，该模型阐明了每个话语如何递归地接收和激活信息通道。此外，我们在一些温和假设下将CCM代数转换为结构因果模型(SCM)，使其与各种因果发现方法兼容。我们进一步提出了SCM的概率实现方法，用于话语级关系推理。通过利用变分推断，该方法探索了隐含原因的代用变量，解决了其不可观测性问题，并通过证据下界重建了话语的因果表征。同时，我们构建了包含隐含原因和完整原因标签的合成及模拟数据集，缓解了当前所有可用数据集均忽略隐含原因的现状。大量实验表明，我们提出的方法在合成、模拟和真实数据集上均显著优于现有方法。最后，我们分析了存在潜在混淆变量时CCM的性能表现，并提出了应对这一未决问题的理论思路。