End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning (2020)

Abstract: Emotion-cause pair extraction (ECPE) is a new task that aims to extract the potential pairs of emotions and their corresponding causes in a document. The existing methods first perform emotion extraction and cause extraction independently, and then perform emotion-cause pairing and filtering. However, the above methods ignore the fact that the cause and the emotion it triggers are inseparable, and the extraction of the cause without specifying the emotion is pathological, which greatly limits the performance of the above methods in the first step. To tackle these shortcomings, we propose two joint frameworks for ECPE: 1) multi-label learning for the extraction of the cause clauses corresponding to the specified emotion clause (CMLL) and 2) multi-label learning for the extraction of the emotion clauses corresponding to the specified cause clause (EMLL). The window of multi-label learning is centered on the specified emotion clause or cause clause and slides as their positions move. Finally, CMLL and EMLL are integrated to obtain the final result. We evaluate our model on a benchmark emotion cause corpus, the results show that our approach achieves the best performance among all compared systems on the ECPE task.

## introduction with the rapid growth of social media, emotion analysis of online text has received much attention in recent years. unlike most studies that focused on the detection and classification of emotions, @xcite first proposed the emotion cause extraction (ece) task, which aims to extract the stimulus behind emotions. @xcite released a public corpus and defined the ece task as a finegrained emotion analysis task, where the goal is to judge for each clause in the document whether it is the corresponding cause, given the annotation of emotions. this corpus has received a lot of attention in subsequent research and has become a benchmark dataset for the ece task @xcite @xcite @xcite . however, there are several inherent shortcomings in the setting of the ece task: firstly, the need for emotion annotation greatly limits the practical applications of the ece task; secondly, the way of annotating emotions before extracting causes ignores the fact that emotions and causes are mutually indicative. to solve these problems, we have proposed the emotion-cause pair extraction (ecpe) task in @xcite , with the goal to extract the potential pairs of emotions and corresponding causes in the document. figure 1 shows an example of the ecpe task. the input is a document, which has been split into six clauses. clauses c4 and c5 are both emotion clauses, which contain the emotion expression "happy" and "worried", respectively. c4 has two corresponding cause clauses: clause c2 ("a policeman visited the old man with the lost money") and clause c3 ("and told him that the thief was caught"). c5 has one corresponding cause clause c6 ("as he doesnt know how to keep so much money"). the output is a set of all emotion-cause pairs in the document: {c4-c2, c4-c3, c5-c6}. the existing ecpe solutions use a two-step framework. step1 independently extracts the emotion clause set and the cause clause set in the document. step2 first applies a cartesian product to the two sets to obtain candidate emotion-cause pairs, and then train a binary classifier to determine whether each candidate pair contains a causal relationship. however, the above methods ignore the fact that the cause and the emotion it triggers are inseparable, and the extraction of the cause without specifying the emotion is pathological, which greatly limits the performance of the above methods in the first step. in this paper, we transform the ecpe task into the emotion-pivot cause extraction problem in the sliding window. specifically, we assume that all clauses in the document are emotion clauses and introduce an emotion-oriented sliding window centered on each emotion clause in the document. then we use a multi-label learning framework inside each sliding window to extract one or more cause clauses that can be paired with the current emotion clause (cmll). taking figure 1 as an example, the clauses c1, c2, • • • , c6 are respectively used as the centers of the emotion-oriented sliding windows, and multi-label learning is performed in each window to extract the corresponding cause clauses: the results for clause c1,c2,c3, and c6 are empty; the result for clause c4 is {c2, c3}; and the result for clause c5 is {c6}. these emotion clauses are then paired with their corresponding cause clauses to get the final emotion-cause pair set: {c4-c2, c4-c3, c5-c6}. on the other hand, we can also transform the ecpe task into the cause-pivot emotion extraction problem in the sliding window by building a causeoriented sliding window centered on each cause clause in the document. then we use a multi-label learning framework inside each sliding window to extract one or more emotion clauses that can be paired with the current cause clause (emll). the above two methods are dual, that is, when we perform emotion-cause pair extraction, we can use emotion clauses as the pivot to extract the corresponding cause clauses, and we can also use the cause clauses as the pivot to extract the corresponding emotion clauses. we get the final emotioncause pairs based on the combination of these two predictions. we evaluate our model on a benchmark emotion cause corpus, the results show that our approach achieves the best performance among all compared systems on the ecpe task. the traditional ece tasks have several inherent shortcomings. first, the need for emotion annotations limits its practical applications. second, annotating emotions before extracting causes ignores the mutual indication of emotions and causes. to solve these shortcomings, we proposed the emotion-cause pair extraction (ecpe) task in @xcite , with the aim to extract all pairs of emotion clauses and corresponding cause clauses in the document. we have further proposed a twostep framework to solve this new task: the first step extracts independent sets of emotion clauses and cause clauses, the second step uses the cartesian product to obtain candidate emotion-cause pairs and introduces a binary classifier to filter out pairs that do not contain causality. it should be noted that @xcite also identifies emotions and causes together, while the task of this paper is different from ours. the main difference is that their task is a joint task of emotion cause extraction and emotion classification (rather than emotion expression extraction), which is based strictly on the condition that the emotion expressions should be annotated in advance. while ours performs automatic extractions of emotion expressions and causes, as well as their pairs. ## conclusion the emotion-cause pair extraction (ecpe) task is a new direction in emotion analysis. to overcome the shortcomings of the existing two-step approach, we propose a sliding window multi-label learning scheme. specifically, we assume that all clauses in the document are emotion clauses, and build an emotion-oriented sliding window centered on each of them. then, in each window, we use the emotion clause as a pivot to extract the corresponding one or more cause clauses based on multi-label learning (cmll). at the same time, we propose the dual form of cmll, i.e., emll, which uses the cause clauses as the pivot to extract the corresponding emotion clauses. the final predictions are obtained by integrating the results of cmll and emll. we evaluated our model on a benchmark emotion cause dataset, and the experimental results show that our method has achieved a substantial improvement over the state-of-the-art method.