The affective reasoning task is a set of emerging affect-based tasks in conversation, including Emotion Recognition in Conversation (ERC),Emotion-Cause Pair Extraction (ECPE), and Emotion-Cause Span Recognition (ECSR). Existing methods make various assumptions on the apparent relationship while neglecting the essential causal model due to the nonuniqueness of skeletons and unobservability of implicit causes. This paper settled down the above two problems and further proposed Conversational Affective Causal Discovery (CACD). It is a novel causal discovery method showing how to discover causal relationships in a conversation via designing a common skeleton and generating a substitute for implicit causes. CACD contains two steps: (i) building a common centering one graph node causal skeleton for all utterances in variable-length conversations; (ii) Causal Auto-Encoder (CAE) correcting the skeleton to yield causal representation through generated implicit causes and known explicit causes. Comprehensive experiments demonstrate that our novel method significantly outperforms the SOTA baselines in six affect-related datasets on the three tasks.

翻译：情感推理任务是一组会话中新兴的基于情感的任务，包括对话情感识别（ERC）、情感-原因对抽取（ECPE）和情感-原因跨度识别（ECSR）。现有方法对表面关系做出了各种假设，但由于骨架非唯一性和隐式原因不可观测性，忽略了本质的因果模型。本文解决了上述两个问题，并进一步提出了对话情感因果发现（CACD）。这是一种新颖的因果发现方法，展示了如何通过设计通用骨架和生成隐式原因的替代项来发现对话中的因果关系。CACD包含两个步骤：（i）为变长对话中的所有语句构建一个以单图节点为中心的通用因果骨架；（ii）因果自编码器（CAE）通过生成的隐式原因和已知显式原因修正骨架以生成因果表示。综合实验表明，我们的方法在三个任务的六个情感相关数据集上显著优于最先进的基线模型。