ECPE -2 D : Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction (2020)

Abstract: In recent years, a new interesting task, called emotion-cause pair extraction (ECPE), has emerged in the area of text emotion analysis. It aims at extracting the potential pairs of emotions and their corresponding causes in a document. To solve this task, the existing research employed a two-step framework, which first extracts individual emotion set and cause set, and then pair the corresponding emotions and causes. However, such a pipeline of two steps contains some inherent flaws: 1) the modeling does not aim at extracting the final emotion-cause pair directly; 2) the errors from the first step will affect the performance of the second step. To address these shortcomings, in this paper we propose a new end-to-end approach, called ECPE-Two-Dimensional (ECPE-2D), to represent the emotion-cause pairs by a 2D representation scheme. A 2D transformer module and two variants, window-constrained and cross-road 2D transformers, are further proposed to model the interactions of different emotion-cause pairs. The 2D representation, interaction, and prediction are integrated into a joint framework. In addition to the advantages of joint modeling, the experimental results on the benchmark emotion cause corpus show that our approach improves the F1 score of the state-of-the-art from 61.28% to 68.89%.

## introduction emotion cause extraction (ece), as a sub-task of emotion analysis, aims at extracting the potential causes of certain emotion expressions in text. the ece task was first proposed by @xcite and defined as a word-level sequence labeling problem. @xcite released a new corpus and re-formalized the ece task as a clause-level extraction problem. given an emotion annotation, * corresponding author the goal of ece is to predict for each clause in a document if the clause is an emotion cause. this framework has received much attention in the following studies in this direction. although the ece task was well defined, it has two problems: firstly, the emotion must be annotated manually before cause extraction, which greatly limits its practical application; secondly, the way to first annotate the emotion and then extract the causes ignores the fact that emotions and causes are mutually indicative. to address this problem, we have proposed a new task named emotion-cause pair extraction (ecpe), aiming to extract the potential pairs of emotions and their corresponding causes together in our previous work @xcite . specifically, ecpe is defined as a fine-grained emotion analysis task, where the goal is to extract a set of valid emotion-cause pairs, given a document consisting of multiple clauses as the input. figure 1 (a) shows an example of the ecpe task. the input in this example is a document consisting of six clauses. clause c4 contains a "happy" emotion and it has two corresponding causes: clause c2 ("a policeman visited the old man with the lost money"), and clause c3 ("told him that the thief was caught"). clause c5 contains a "worried" emotion and the corresponding cause is clause c6 ("as he doesn't know how to keep so much money"). the final output is a set of valid emotion-cause pairs defined at clause level: {c4-c2, c4-c3, c5-c6}. we have also proposed a two-step approach (ecpe-2steps) to address the ecpe task @xcite . ecpe-2steps is a pipeline of two steps: step 1 extracts an emotion set and a cause set individually. for example in figure 1 : {c4-c2 , c4-c3 , c4-c6 , c5-c2 , c5-c3 , c5-c6} step 2 -filtering c1: yesterday morning, c2: a policeman visited the old man with the lost money, c3: and told him that the thief was caught. c4: the old man was very happy. c5: but he still feels worried, c6: as he doesn't know how to keep so much money. ## related work the emotion-cause pair extraction (ecpe) task was first proposed in our prior work @xcite and is derived from the traditional emotion cause extraction (ece) task. since the ecpe task was recently proposed, there is little work on it. we mainly introduce the related work of ece task. the emotion cause extraction (ece) task was first proposed by @xcite , with the goal to extract the word-level causes that lead to the given emotions in text. based on the same task settings, there were some other individual studies that conducted ece research on their own corpus us-ing rule-based methods @xcite @xcite or machine learning methods @xcite . based on the analysis of the corpus in (lee et al., 2010), @xcite suggested that a clause may be the most appropriate unit to detect causes and transformed the task from word-level to clauselevel. there was also some work based on this task setting @xcite . recently, a chinese emotion cause dataset was released by @xcite , and has received much attention. based on this corpus, a lot of traditional machine learning methods @xcite and deep learning methods @xcite @xcite @xcite were proposed. in addition, there is also some work focused on cause detection for chinese microblogs using a multiple-user structure and formalized two cause detection tasks for microblogs (current-subtweetbased cause detection and original-subtweet-based cause detection). @xcite . the traditional ece tasks suffer from two shortcomings: 1) the emotion must be annotated before cause extraction in ece, which greatly limits its applications in real-world scenarios; 2) the way to first annotate emotion and then extract the cause ignores the fact that they are mutually indicative. to address this problem, we proposed the new emotion-cause pair extraction task in @xcite , which aims to extract the potential pairs of emotions and corresponding causes in a document. we have also proposed a two-step framework, which first extracts individual emotion set and cause set, and then pairs the corresponding emotions and causes. in this paper, we propose a new end-to-end approach to represent the emotion-cause pairs by a 2d representation scheme. two kinds of 2d transformers, namely window-constrained and cross-road 2d transformers, are further proposed to model the interactions of different emotion-cause pairs. finally, the 2d representation, interaction, and prediction are integrated into a joint framework. ## conclusions the emotion-cause pair extraction (ecpe) task has drawn attention recently. however the previous approach employed a two-step pipeline framework and has some inherent flaws. in this paper, instead of a pipeline of two steps, we propose a joint endto-end framework, called ecpe-2d, to represent the emotion-cause pairs by a 2d representation scheme, and integrate the 2d emotion-cause pair representation, interaction, and prediction into a joint a framework. we also develop two kinds of 2d transformers, i.e., window-constrained and cross-road 2d transformers, to further model the interaction of different emotion-cause pairs. the experimental results on the benchmark emotion cause corpus demonstrate that in addition to the advantages of joint modeling, our approach outperforms the state-of-the-art method by 7.6 percentage points in terms of the f1 score on the ecpe task.