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研究的关键方面，尚未被包括大型语言模型在内的主流模型充分解决。作为其重要组成部分的对话推理，由于缺乏精心设计的认知模型而基本未被探索。本文受对话认知直觉理论的启发，开发了一种对话认知模型（CCM），该模型解释了每个话语如何递归地接收和激活信息通道。此外，在温和假设下，我们将CCM代数转化为结构化因果模型（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。通过利用变分推理，该方法探索了隐含因果的替代项，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。此外，我们构建了包含隐含因果和完整因果标签的合成与模拟数据集，缓解了现有数据集均忽视隐含因果的现状。大量实验表明，所提方法在合成、模拟及真实世界数据集上显著优于现有方法。最终，我们分析了CCM在潜在混淆因素下的性能，并提出了解决这一当前未解决问题的理论思路。