A Symmetric Local Search Network for Emotion-Cause Pair Extraction (2020)

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 individually, then paired the emotion and cause clauses, and filtered out the pairs without causality. 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.

## introduction emotion cause analysis is a research branch of sentiment analysis and has gained increasing popularity in recent years @xcite @xcite . 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 @xcite @xcite @xcite @xcite . 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, @xcite 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 c 4 , c 13 , and c 17 (marked as orange), and their corresponding cause clauses are c 3 , c 12 , and c 15 (marked as blue). the goal of ecpe task is to extract all emotion-cause pairs: (c 4 , c 3 ), (c 13 , c 12 ), and (c 17 , c 15 ). the ecpe task is a new and more challenging task. to tackle this task, @xcite 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. 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., c 4 ), humans will search its corresponding cause clause (i.e., c 3 ) within its local context (i.e., c 2 , c 3 , c 4 , c 5 , c 6 ). the advantage of local search is that the wrong pairs (e.g., (c 4 , c 12 )) 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., (c 13 , c 15 )) 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: 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. … 𝑷 𝒇𝒊𝒏𝒂𝒍 𝑷 𝒆𝒍𝒄 𝑜𝑟 𝑷 𝒄𝒍𝒆 𝑜𝑟 𝑷 𝒆𝒍𝒄 ∩ 𝑷 𝒄𝒍𝒆 𝑜𝑟 (𝑷 𝒆𝒍𝒄 ∪ 𝑷 𝒄𝒍𝒆 ) c 1 … c i … c n symmetrical local search network (slsn) 𝑷 𝒆𝒍𝒄 = ⋯ , (c 𝒆 , 𝒄 𝒍𝒄 , ⋯ } ෝ 𝒚 𝒊 𝒆𝒍𝒄 = ( ො 𝑦 𝑖 𝑒 , ො 𝑦 𝑖-1 𝑙𝑐 , ො 𝑦 𝑖 𝑙𝑐 , ො 𝑦 𝑖+1 𝑙𝑐 ) ෝ 𝒚 𝒊 𝒄𝒍𝒆 = ( ො 𝑦 𝒊 𝒄 , ො 𝑦 𝒊-𝟏 𝒍𝒆 , ො 𝑦 𝒊 𝒍𝒆 , ො 𝑦 𝒊+𝟏 𝒍𝒆 ) 𝑷 𝒄𝒍𝒆 = ⋯ , (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 @xmath0, c 2 , • • • , c n ]. 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 , c c ). the goal of ecpe task is to extract all emotion-cause pairs in d: 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. ## related work emotion cause analysis has been studied for about a decade @xcite @xcite . previous studies on emotion cause analysis mainly focused on the emotion cause extraction (ece) task @xcite @xcite . recently, based on the ece task, a new and more challenging task named emotion cause pair extraction (ecpe) was proposed @xcite . the ece task was first proposed by @xcite and was formalized as a word-level sequence labeling problem. but @xcite suggested that clause may be a more appropriate unit than word for detecting cause. later, based on this idea, @xcite released a chinese ece corpus from a public sina city news. in this corpus, the ece task was defined as a clause-level sequence labeling problem, the objective of which is to predict the cause clauses in a document given the emotion. for the following studies on ece task, this corpus has become a benchmark dataset. while early studies mainly adopted the rule-based methods @xcite @xcite and machine learning methods @xcite to deal with the ece task, recent studies has begun to apply the deep learning methods to this task @xcite @xcite @xcite @xcite . although the ece task is valuable in practice, its application in real-world scenarios is limited due to the reason that the emotion clauses are naturally not annotated. considering this situation, @xcite proposed the ecpe task and released a corresponding ecpe dataset based on the ece corpus. to tackle the task, they proposed a two-step method, which first extracted emotions and causes individually by multi-task framework, and then got the emotion-cause pairs by pairing and filtering. ## conclusion and future work in this paper, we propose a symmetric local search network (slsn) to perform end-to-end emotion-cause pair extraction. slsn can straightly extract the emotion-cause pair through a process of local search. this is realized by designing a special component, i.e., local pair searcher, which allows simultaneously detecting and matching the emotions and causes. experimental results on the ecpe corpus demonstrate the effectiveness of our model. in the future, we will consider to further improve the performance of emotion extraction and cause extraction by employing more powerful pre-trained encoder (e.g., bert @xcite ) or designing some auxiliary tasks to utilize extra knowledge. besides, we will further explore the process of local pair search, and seek for more advanced implementations of the local pair searcher.