A Symmetric Local Search Network for Emotion-Cause Pair Extraction Zifeng Cheng,Zhiwei Jiang",Yafeng Yin,Hua Yu,Qing Gu State Key Laboratory for Novel Software Technology, Nanjing University,Nanjing 210023,China chengzf@smail.nju.edu.cn,[jzw,yafeng@nju.edu.cn huayu.yhesmail.nju.edu.cn,guq@nju.edu.cn Abstract Emotion-cause pair extraction(ECPE)is a new task which aims at extracting the potential clause pairs of emotions and corresponding causes in a document.To tackle this task,a two-step method was proposed by previous study which first extracted emotion clauses and cause clauses indi- vidually,then paired the emotion and cause clauses,and filtered out the pairs without causal- ity.Different from this method that separated the detection and the matching of emotion and cause into two steps,we propose a Symmetric Local Search Network(SLSN)model to perform the detection and matching simultaneously by local search.SLSN consists of two symmetric subnetworks,namely the emotion subnetwork and the cause subnetwork.Each subnetwork is composed of a clause representation learner and a local pair searcher.The local pair searcher is a specially-designed cross-subnetwork component which can extract the local emotion-cause pairs.Experimental results on the ECPE corpus demonstrate the superiority of our SLSN over existing state-of-the-art methods. 1 Introduction Emotion cause analysis is a research branch of sentiment analysis and has gained increasing popularity in recent years (Lee et al.,2010;Gui et al.,2016;Xia and Ding,2019;Xia et al.,2019).Its goal is to identify the potential causes that lead to the certain emotion.This is very useful in fields such as electronic commerce,where the sellers may concern about users'emotions towards the products as well as the causes of users'emotions. Previous studies on emotion cause analysis mainly focus on the task of emotion cause extraction (ECE),which is usually formalized as a clause-level sequence labeling problem(Chen et al.,2010;Gui et al.,2016;Li et al.,2018;Ding et al.,2019;Xia et al.,2019;Yu et al.,2019;Fan et al.,2019). Given an annotated emotion clause,for each clause in the document,the goal of ECE task is to identify whether the clause is the corresponding cause.However,in practice,the emotion clauses are naturally not annotated,which may limit the application of the ECE task in real-world scenarios.Motivated by this,Xia and Ding (2019)first proposed the emotion-cause pair extraction(ECPE)task,which aims to extract all potential pairs of emotion and corresponding cause in a document.As shown in Figure 1,the example document has 17 clauses,among which,the emotion clauses are c4,c13,and c17(marked as orange),and their corresponding cause clauses are c3,c12,and c15(marked as blue).The goal of ECPE task is to extract all emotion-cause pairs:(c4,c3),(c13,c12),and (c17,c15). The ECPE task is a new and more challenging task.To tackle this task,Xia and Ding(2019)proposed a two-step method,which has been demonstrated to be effective.In the first step,they extracted emotion clauses and cause clauses individually.In the second step,they used Cartesian product to pair the clauses and then used a logistic regression to filter out the emotion-cause pairs without causality.In this method, the detection of emotion and cause,and the matching of emotion and cause are separately implemented in two steps. Corresponding Author This work is licensed under a Creative Commons Attribution 4.0 International Licence.Licence details:http: //creativecommons.org/licenses/by/4.0/. 139 Proceedings of the 28th International Conference on Computational Linguistics,pages 139-149 Barcelona,Spain(Online),December 8-13,2020Proceedings of the 28th International Conference on Computational Linguistics, pages 139–149 Barcelona, Spain (Online), December 8-13, 2020 139 A Symmetric Local Search Network for Emotion-Cause Pair Extraction Zifeng Cheng, Zhiwei Jiang∗ , Yafeng Yin, Hua Yu, Qing Gu State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China chengzf@smail.nju.edu.cn,{jzw,yafeng}@nju.edu.cn huayu.yh@smail.nju.edu.cn,guq@nju.edu.cn Abstract Emotion-cause pair extraction (ECPE) is a new task which aims at extracting the potential clause pairs of emotions and corresponding causes in a document. To tackle this task, a two-step method was proposed by previous study which first extracted emotion clauses and cause clauses indi￾vidually, then paired the emotion and cause clauses, and filtered out the pairs without causal￾ity. Different from this method that separated the detection and the matching of emotion and cause into two steps, we propose a Symmetric Local Search Network (SLSN) model to perform the detection and matching simultaneously by local search. SLSN consists of two symmetric subnetworks, namely the emotion subnetwork and the cause subnetwork. Each subnetwork is composed of a clause representation learner and a local pair searcher. The local pair searcher is a specially-designed cross-subnetwork component which can extract the local emotion-cause pairs. Experimental results on the ECPE corpus demonstrate the superiority of our SLSN over existing state-of-the-art methods. 1 Introduction Emotion cause analysis is a research branch of sentiment analysis and has gained increasing popularity in recent years (Lee et al., 2010; Gui et al., 2016; Xia and Ding, 2019; Xia et al., 2019). Its goal is to identify the potential causes that lead to the certain emotion. This is very useful in fields such as electronic commerce, where the sellers may concern about users’ emotions towards the products as well as the causes of users’ emotions. Previous studies on emotion cause analysis mainly focus on the task of emotion cause extraction (ECE), which is usually formalized as a clause-level sequence labeling problem (Chen et al., 2010; Gui et al., 2016; Li et al., 2018; Ding et al., 2019; Xia et al., 2019; Yu et al., 2019; Fan et al., 2019). Given an annotated emotion clause, for each clause in the document, the goal of ECE task is to identify whether the clause is the corresponding cause. However, in practice, the emotion clauses are naturally not annotated, which may limit the application of the ECE task in real-world scenarios. Motivated by this, Xia and Ding (2019) first proposed the emotion-cause pair extraction (ECPE) task, which aims to extract all potential pairs of emotion and corresponding cause in a document. As shown in Figure 1, the example document has 17 clauses, among which, the emotion clauses are c4, c13, and c17 (marked as orange), and their corresponding cause clauses are c3, c12, and c15 (marked as blue). The goal of ECPE task is to extract all emotion-cause pairs: (c4, c3), (c13, c12), and (c17, c15). The ECPE task is a new and more challenging task. To tackle this task, Xia and Ding (2019) proposed a two-step method, which has been demonstrated to be effective. In the first step, they extracted emotion clauses and cause clauses individually. In the second step, they used Cartesian product to pair the clauses and then used a logistic regression to filter out the emotion-cause pairs without causality. In this method, the detection of emotion and cause, and the matching of emotion and cause are separately implemented in two steps. ∗ Corresponding Author This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http: //creativecommons.org/licenses/by/4.0/