Emotion-cause pair extraction (ECPE) is an emerging task aiming to extract potential pairs of emotions and corresponding causes from documents. Previous approaches have focused on modeling the pair-to-pair relationship and achieved promising results. However, the clause-to-clause relationship, which fundamentally symbolizes the underlying structure of a document, has still been in its research infancy. In this paper, we define a novel clause-to-clause relationship. To learn it applicably, we propose a general clause-level encoding model named EA-GAT comprising E-GAT and Activation Sort. E-GAT is designed to aggregate information from different types of clauses; Activation Sort leverages the individual emotion/cause prediction and the sort-based mapping to propel the clause to a more favorable representation. Since EA-GAT is a clause-level encoding model, it can be broadly integrated with any previous approach. Experimental results show that our approach has a significant advantage over all current approaches on the Chinese and English benchmark corpus, with an average of $2.1\%$ and $1.03\%$.
翻译:情感成因提取(ECPE)是一项新兴任务,旨在从文件中提取潜在的情感和相应的成因。以前的方法侧重于对对对对对对关系进行建模,并取得了有希望的成果。然而,从根本上象征着文件基本结构的条款对锁关系仍然处于其研究的初级阶段。在本文中,我们定义了一种新颖的条款对锁的关系。为了了解这一点,我们提议了一个一般性条款级编码模式,名为EA-GAT, 包括E-GAT和Sort 。E-GAT旨在汇总不同类型条款的信息;“行动分类”利用个人情感/原因预测和排序绘图将条款推向更有利的表述。由于EA-GAT是一个条款级的编码模式,它可以与以往的任何方法广泛结合。实验结果表明,我们的方法对中英基准集的所有现行方法都有很大的优势,平均为2.1美元和1.03美元。