(c)But when Hans heard these, (c2)he seemed very jealous. (c:)When Mr.Song had a son, (c)Hans was also very happy. (cs c3) (c)Hans had taught him to speak English'since theboy was young. (c)Hans also speaks Spanish and German, (c)and he ofen went downstairs to the community toteach children English (c)During Martial Arts Festival, (cs c12)X (c)he also helped with a lot of translation work, (c)and was rated as advanced worker. (cu)After the meeting. (cthe city organized all participants to travel, (c)Hans was very excited.- (C13c2)√ (c4)But before getting on the bus, (c13,c1s)X (cs)the tour guide said he was too old to go (c6)Everyone can see, (cr7 C1s) (c)Hans was very lost Figure 1:An example document from the ECPE corpus However,when humans deal with the ECPE task,they usually consider the detection and matching problems at the same time.This is mainly achieved through the process of local search.For example, as shown in Figure 1,if a clause is detected as an emotion clause (e.g.,c4),humans will search its corresponding cause clause (i.e.,ca)within its local context (i.e.,c2,c3,c4,c5,c6).The advantage of local search is that the wrong pairs (e.g.,(c4,c12))beyond the local context scope can be avoided Additionally,when local searching the cause clause corresponding to the target emotion clause,humans not only judge whether the clause is a cause clause,but also consider whether it matches the target emotion clause.In this way,they can avoid extracting the pairs (e.g.,(c13,c15))that are in the local context scope but mismatch.Similarly,when a cause clause is encountered,the corresponding emotion clause can also be searched within its local context scope. Inspired by this local search process,we propose a Symmetric Local Search Network (SLSN)model. The model consists of two subnetworks with symmetric structures,namely the emotion subnetwork and the cause subnetwork.Each subnetwork consists of two parts:a clause representation learner and a local pair searcher (LPS).Among them,the clause representation learner is designed to learn the emotion or cause representation of a clause.The local pair searcher is designed to perform the local search of emotion-cause pairs.Specifically,the LPS introduces a local context window to limit the scope of context for local search.In the process of local search,the LPS first judges whether the target clause is emotion (cause),and then judges whether each clause within the local context window is the corresponding cause (emotion).Finally,SLSN will output the local pair labels (i.e.,the labels of the target clause and the clauses within its local context window)for each clause in the document,from which we can get the emotion-cause pairs. The main contributions of this work can be summarized as follows: We propose a symmetric local search network model,which is an end-to-end model and gives a new scheme to solve the ECPE task. We design a local pair searcher in SLSN,which allows simultaneously detecting and matching the emotions and causes. Experimental results on the ECPE corpus demonstrate the superiority of our SLSN over existing state-of-the-art methods. 2 Symmetric Local Search Network In this section,we first present the task definition.Then,we introduce the SLSN model,followed by its technical details.Finally,we discuss the connection between the SLSN model and the previous two-step method. 140140 (c1 ) But when Hans heard these, (c2 ) he seemed very jealous. (c3 ) When Mr. Song had a son, (c4 ) Hans was also very happy. (c5 ) Hans had taught him to speak English since the boy was young. (c6) Hans also speaks Spanish and German, (c7 ) and he often went downstairs to the community to teach children English. (c8 ) During Martial Arts Festival, (c9 ) he also helped with a lot of translation work, (c10) and was rated as advanced worker. (c11) After the meeting, (c12) the city organized all participants to travel, (c13) Hans was very excited. (c14) But before getting on the bus, (c15) the tour guide said he was too old to go. (c16) Everyone can see, (c17) Hans was very lost. (c4 , c3) (c13, c12) (c17, c15) (c4 , c12) (c13, c15) Figure 1: An example document from the ECPE corpus However, when humans deal with the ECPE task, they usually consider the detection and matching problems at the same time. This is mainly achieved through the process of local search. For example, as shown in Figure 1, if a clause is detected as an emotion clause (e.g., c4), humans will search its corresponding cause clause (i.e., c3) within its local context (i.e., c2, c3, c4, c5, c6). The advantage of local search is that the wrong pairs (e.g., (c4, c12)) beyond the local context scope can be avoided. Additionally, when local searching the cause clause corresponding to the target emotion clause, humans not only judge whether the clause is a cause clause, but also consider whether it matches the target emotion clause. In this way, they can avoid extracting the pairs (e.g., (c13, c15)) that are in the local context scope but mismatch. Similarly, when a cause clause is encountered, the corresponding emotion clause can also be searched within its local context scope. Inspired by this local search process, we propose a Symmetric Local Search Network (SLSN) model. The model consists of two subnetworks with symmetric structures, namely the emotion subnetwork and the cause subnetwork. Each subnetwork consists of two parts: a clause representation learner and a local pair searcher (LPS). Among them, the clause representation learner is designed to learn the emotion or cause representation of a clause. The local pair searcher is designed to perform the local search of emotion-cause pairs. Specifically, the LPS introduces a local context window to limit the scope of context for local search. In the process of local search, the LPS first judges whether the target clause is emotion (cause), and then judges whether each clause within the local context window is the corresponding cause (emotion). Finally, SLSN will output the local pair labels (i.e., the labels of the target clause and the clauses within its local context window) for each clause in the document, from which we can get the emotion-cause pairs. The main contributions of this work can be summarized as follows: • We propose a symmetric local search network model, which is an end-to-end model and gives a new scheme to solve the ECPE task. • We design a local pair searcher in SLSN, which allows simultaneously detecting and matching the emotions and causes. • Experimental results on the ECPE corpus demonstrate the superiority of our SLSN over existing state-of-the-art methods. 2 Symmetric Local Search Network In this section, we first present the task definition. Then, we introduce the SLSN model, followed by its technical details. Finally, we discuss the connection between the SLSN model and the previous two-step method