DualECot: Toward Explainable Emotion Causality Analysis in Conversations

Emotion Causality Analysis in Conversations is a critical research field that provides deep insights into the psychological and behavioral dynamics of individuals during interactions. Previous research has typically framed emotion causality analysis in conversations as an utterance-level classification problem. However, this coarse-grained approach often results in models capturing only superficial associations between emotional utterances and their cause utterances, rather than understanding the genuine causal relationships. This limitation undermines the generalizability of these models. However, this coarse-grained methodology often leads to models merely capturing the superficial correlations between emotional expressions and their corresponding cause statements, instead of delving into and comprehending the authentic causal connections. This limitation undermines the generalizability of these models. Additionally, existing methods are often criticized for their lack of interpretability, which significantly restricts their practical applications. In this paper, we introduce DualECot, a novel dual-view reasoning framework that leverages Chain-of-Thought to comprehensively analyze the emotional dynamics in conversations. In this paper, we present DualECot, a novel dual-view reasoning framework. By harnessing Chain- of- Thought, it enables a comprehensive analysis of emotional dynamics in conversations. Specifically, the framework consists of two complementary reasoning chains. The first, Diving into One's Heart, pinpoints potential emotion triggers and examines the target speaker's psychological state to uncover the process behind emotion evocation. The second, Stepping into Others' Shoes, investigates the emotional ripple effects of the target utterance on subsequent dialogue interactions. To tackle the shortage of interpretable reasoning data, we annotated reasoning chains on two benchmark datasets. We then assessed the framework's effectiveness via in-context learning and supervised experiments. Experimental results demonstrate that DualECot not only achieves superior performance, but also enhances explainability and generalization capabilities. We will release our annotation as an open resource for the community.Specifically, the framework consists of two complementary reasoning chains. The first, Diving into One's Heart, pinpoints potential emotion triggers and examines the target speaker's psychological state to uncover the process behind emotion evocation. The second, Stepping into Others' Shoes, investigates the emotional ripple effects of the target utterance on subsequent dialogue interactions. To tackle the shortage of interpretable reasoning data, we annotated reasoning chains on two benchmark datasets. We then assessed the framework's effectiveness via in-context learning and supervised experiments. Experimental results demonstrate that DualECot not only achieves superior performance, but also enhances explainability and generalization capabilities. We will release our annotation as an open resource for the community.