Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1003–1012 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 1003 Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts Rui Xia, Zixiang Ding School of Computer Science and Engineering, Nanjing University of Science and Technology, China {rxia, dingzixiang}@njust.edu.cn Abstract Emotion cause extraction (ECE), the task aimed at extracting the potential causes behind certain emotions in text, has gained much at- tention in recent years due to its wide applica- tions. However, it suffers from two shortcom- ings: 1) the emotion must be annotated before cause extraction in ECE, which greatly limits its applications in real-world scenarios; 2) the way to first annotate emotion and then extract the cause ignores the fact that they are mutual- ly indicative. In this work, we propose a new task: emotion-cause pair extraction (ECPE), which aims to extract the potential pairs of e- motions and corresponding causes in a docu- ment. We propose a 2-step approach to ad- dress this new ECPE task, which first perform- s individual emotion extraction and cause ex- traction via multi-task learning, and then con- duct emotion-cause pairing and filtering. The experimental results on a benchmark emotion cause corpus prove the feasibility of the ECPE task as well as the effectiveness of our ap- proach. 1 Introduction Emotion cause extraction (ECE) aims at extracting potential causes that lead to emotion expression- s in text. The ECE task was first proposed and defined as a word-level sequence labeling prob- lem in Lee et al. (2010). To solve the shortcom- ing of extracting causes at word level, Gui et al. (2016a) released a new corpus which has received much attention in the following study and become a benchmark dataset for ECE research. Figure 1 displays an example from this corpus, There are five clauses in a document. The emotion “happy” is contained in the fourth clause. We de- note this clause as emotion clause, which refers to a clause that contains emotions. It has two corre- sponding causes: “a policeman visited the old man with the lost money” in the second clause, and “told him that the thief was caught” in the third clause. We denote them as cause clause, which refers to a clause that contains causes. The ECE task was formalized as a clause-level binary classification problem in Gui et al. (2016a). The goal is to detect for each clause in a document, whether this clause is a cause given the annotation of emotion. This framework was followed by most of the recent studies in this field (Lee et al., 2010; Gui et al., 2016a; Li et al., 2018; Xu et al., 2019; Yu et al., 2019). However, there are two shortcomings in the cur- rent ECE task. The first is that emotions must be annotated before cause extraction in the test set, which limits the applications of ECE in real-world scenarios. The second is that the way to first …

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