Pfinal Pete or Pete or (Pete nPete)or (Pete UPete) Per={…,(ce,dc)…J Pc={,(cle,cc).…} =,驰%，脂) =5,张1，，骆1) ■回…■ Symmetrical Local Search Network(SLSN) Figure 2:Overview of SLSN model 2.1 Task Definition The task of emotion-cause pair extraction(ECPE)is first studied by Xia and Ding (2019).In the ECPE task,each document d in the dataset D consists of multiple clauses d=[c1,c2,..,cn].The clause with emotional polarity(such as happiness,sadness,fear,anger,disgust and surprise)is labeled as an emotion clause ce.The clause that causes the emotion is called a cause clause ce.The pair of emotion clause and its corresponding cause clause is called an emotion-cause pair(ce,c).The goal of ECPE task is to extract all emotion-cause pairs in d: P={…,(c,c),…} Note that each document may contain several (at least one)emotion clauses,and each emotion clause may correspond to several (at least one)cause clauses.Besides,the emotion clause and its corresponding cause clause may be the same clause. 2.2 An Overview of SLSN As shown in Figure 2,SLSN receives a sequence of clauses from a document as input and predicts the local pair labels for these clauses,which can be directly converted into the corresponding emotion-cause (E-C)pairs.For each clause ci,SLSN predicts two types of local pair labels:E-LC labele and C- LE label le.The E-LC label le contains the emotion label (E-label)of the i-th clause and the local cause labels (LC-label)()of the clauses near the i-th clause.Similarly,the C-LE label e contains the cause label (C-label)of the i-th clause and the local emotion labels(LE-label) (,,of the clauses near the i-th clause.Whether a clause is near the target clause is defined by the local context window,whose size is denoted as k (the case in Figure 2 is k 1).That is,for a target clause,the scope of its local context includes the previous k clauses,itself,and the following clauses.Note that,both the E-LC labelle and the C-LE label e can be converted into their corresponding emotion-cause(E-C)pairs.For example,the corresponding E-C pair of e=(1,1,0,0) is (ci,ci-1),and the corresponding E-C pair of le =(1,1,0,0)is (ci-1,ci).We denote the E-C pair set corresponding toe as Pelc,and the E-C pair set corresponding to le as Pele.Then the final E-C pair set of our method is the union of Pelc and Pdle.Of course,Pele,Pcle or the intersection of Pele and Ple is also an option for the final pair set. 2.3 Components of SLSN As shown in Figure 3,SLSN contains two subnetworks,i.e.,the emotion subnetwork referred as E-net which is mainly for the E-LC label prediction and the cause subnetwork referred as C-net which is mainly for the C-LE label prediction.E-net and C-net have similar structures in terms of word embedding,clause encoder,and hidden state learning.After the hidden state learning layer,E-net and C-net use two types of local pair searchers (LPS)with symmetric structures for the local pair label prediction.The local pair 141141 … 𝑷𝒇𝒊𝒏𝒂𝒍 𝑷𝒆𝒍𝒄 𝑜𝑟 𝑷𝒄𝒍𝒆 𝑜𝑟 𝑷𝒆𝒍𝒄 ∩ 𝑷𝒄𝒍𝒆 𝑜𝑟 (𝑷𝒆𝒍𝒄 ∪ 𝑷𝒄𝒍𝒆) c1 … ci … cn Symmetrical Local Search Network (SLSN) 𝑷𝒆𝒍𝒄 = ⋯ , (c 𝒆 , 𝒄 𝒍𝒄 , ⋯ } 𝒚ෝ𝒊 𝒆𝒍𝒄 = (𝑦ො𝑖 𝑒 , 𝑦ො𝑖−1 𝑙𝑐 , 𝑦ො𝑖 𝑙𝑐 , 𝑦ො𝑖+1 𝑙𝑐 ) 𝒚ෝ𝒊 𝒄𝒍𝒆 = (𝑦ො𝒊 𝒄 , 𝑦ො𝒊−𝟏 𝒍𝒆 , 𝑦ො𝒊 𝒍𝒆 , 𝑦ො𝒊+𝟏 𝒍𝒆 ) 𝑷𝒄𝒍𝒆 = ⋯ , (c 𝒍𝒆 , 𝒄 𝒄 , ⋯ } … … … Figure 2: Overview of SLSN model 2.1 Task Definition The task of emotion-cause pair extraction (ECPE) is first studied by Xia and Ding (2019). In the ECPE task, each document d in the dataset D consists of multiple clauses d = [c1, c2, · · · , cn]. The clause with emotional polarity (such as happiness, sadness, fear, anger, disgust and surprise) is labeled as an emotion clause c e . The clause that causes the emotion is called a cause clause c c . The pair of emotion clause and its corresponding cause clause is called an emotion-cause pair (c e , cc ). The goal of ECPE task is to extract all emotion-cause pairs in d: P = {· · · ,(c e , cc ), · · · } Note that each document may contain several (at least one) emotion clauses, and each emotion clause may correspond to several (at least one) cause clauses. Besides, the emotion clause and its corresponding cause clause may be the same clause. 2.2 An Overview of SLSN As shown in Figure 2, SLSN receives a sequence of clauses from a document as input and predicts the local pair labels for these clauses, which can be directly converted into the corresponding emotion-cause (E-C) pairs. For each clause ci , SLSN predicts two types of local pair labels: E-LC label yˆ elc i and C￾LE label yˆ cle i . The E-LC label yˆ elc i contains the emotion label (E-label) yˆ e i of the i-th clause and the local cause labels (LC-label) (ˆy lc i−1 , yˆ lc i , yˆ lc i+1) of the clauses near the i-th clause. Similarly, the C-LE label yˆ cle i contains the cause label (C-label) yˆ c i of the i-th clause and the local emotion labels (LE-label) (ˆy le i−1 , yˆ le i , yˆ le i+1) of the clauses near the i-th clause. Whether a clause is near the target clause is defined by the local context window, whose size is denoted as k (the case in Figure 2 is k = 1). That is, for a target clause, the scope of its local context includes the previous k clauses, itself, and the following k clauses. Note that, both the E-LC label yˆ elc i and the C-LE label yˆ cle i can be converted into their corresponding emotion-cause (E-C) pairs. For example, the corresponding E-C pair of yˆ elc i = (1, 1, 0, 0) is (ci , ci−1), and the corresponding E-C pair of yˆ cle i = (1, 1, 0, 0) is (ci−1, ci). We denote the E-C pair set corresponding to yˆ elc i as Pelc, and the E-C pair set corresponding to yˆ cle i as Pcle. Then the final E-C pair set of our method is the union of Pelc and Pcle. Of course, Pelc, Pcle or the intersection of Pelc and Pcle is also an option for the final pair set. 2.3 Components of SLSN As shown in Figure 3, SLSN contains two subnetworks, i.e., the emotion subnetwork referred as E-net which is mainly for the E-LC label prediction and the cause subnetwork referred as C-net which is mainly for the C-LE label prediction. E-net and C-net have similar structures in terms of word embedding, clause encoder, and hidden state learning. After the hidden state learning layer, E-net and C-net use two types of local pair searchers (LPS) with symmetric structures for the local pair label prediction. The local pair