<p>Recognizing the fine-grained emotion cause extraction-Causal Span Extraction (CSE)-is challenging due to the more detailed analysis of contextual information. The previous research on CSE has focused on the powerful semantic representation capability of PLMs while overlooking the semantic coherence of the speaker and content when emotions arise in conversation. In this paper, we introduce a novel method by learning contextual information and enhancing the consistency of cross-task alignment. Specifically, we integrate the coreference resolution into the attention mechanism to capture the coreference-aware semantic correlations and employ the position relation strategy at both the utterance and token levels to understand the contextual information. Furthermore, by incorporating auxiliary tasks and a novel cross-task alignment approach, we reduce inconsistent predictions across tasks, thereby enabling a comprehensive, multi-dimensional comprehension of conversations. Our method demonstrates a marked improvement over current state-of-the-art models, evidenced by superior performance on two benchmark datasets.</p>