Emotion-Cause Pair Extraction (ECPE) aims to extract all emotion clauses and their corresponding cause clauses from a document. Existing approaches tackle this task through multi-task learning (MTL) framework in which the two subtasks provide indicative clues for ECPE. However, the previous MTL framework considers only one round of multi-task reasoning and ignores the reverse feedbacks from ECPE to the subtasks. Besides, its multi-task reasoning only relies on semantics-level interactions, which cannot capture the explicit dependencies, and both the encoder sharing and multi-task hidden states concatenations can hardly capture the causalities. To solve these issues, we first put forward a new MTL framework based on Co-evolving Reasoning. It (1) models the bidirectional feedbacks between ECPE and its subtasks; (2) allows the three tasks to evolve together and prompt each other recurrently; (3) integrates prediction-level interactions to capture explicit dependencies. Then we propose a novel multi-task relational graph (MRG) to sufficiently exploit the causal relations. Finally, we propose a Co-evolving Graph Reasoning Network (CGR-Net) that implements our MTL framework and conducts Co-evolving Reasoning on MRG. Experimental results show that our model achieves new state-of-the-art performance, and further analysis confirms the advantages of our method.

翻译：情感-原因对抽取（ECPE）旨在从文档中提取所有的情感子句及其对应的原因子句。现有方法通过多任务学习（MTL）框架来处理该任务，其中两个子任务为ECPE提供指示性线索。然而，先前的MTL框架仅考虑单轮多任务推理，忽略了从ECPE到子任务的反向反馈。此外，其多任务推理仅依赖语义层面的交互，无法捕捉显式的依赖关系，且编码器共享与多任务隐状态拼接难以获取因果关系。为解决这些问题，我们首先提出一种基于协同演化推理的新MTL框架。该框架能够：（1）建模ECPE与其子任务之间的双向反馈；（2）使三个任务协同演化并循环相互促进；（3）整合预测层面的交互以捕捉显式依赖。随后，我们提出一种新颖的多任务关系图（MRG）以充分利用因果关联。最后，我们提出协同演化图推理网络（CGR-Net），该网络实现了我们的MTL框架，并在MRG上进行协同演化推理。实验结果表明，我们的模型取得了新的最优性能，进一步分析证实了本方法的优势。