Joint Constrained Learning with Boundary-adjusting for Emotion-Cause Pair Extraction (2023)

Abstract: Emotion-Cause Pair Extraction (ECPE) aims to identify the document’s emotion clauses and corresponding cause clauses. Like other relation extraction tasks, ECPE is closely associated with the relationship between sentences. Recent methods based on Graph Convolutional Networks focus on how to model the multiplex relations between clauses by constructing different edges. However, the data of emotions, causes, and pairs are extremely unbalanced, and current methods get their representation using the same graph structure. In this paper, we propose a **J**oint **C**onstrained Learning framework with **B**oundary-adjusting for Emotion-Cause Pair Extraction (**JCB**). Specifically, through constrained learning, we summarize the prior rules existing in the data and force the model to take them into consideration in optimization, which helps the model learn a better representation from unbalanced data. Furthermore, we adjust the decision boundary of classifiers according to the relations between subtasks, which have always been ignored. No longer working independently as in the previous framework, the classifiers corresponding to three subtasks cooperate under the relation constraints. Experimental results show that **JCB** obtains competitive results compared with state-of-the-art methods and prove its robustness on unbalanced data.

## introduction emotion cause analysis aims to capture causal relationships between human emotions and their corresponding causes, which has drawn extensive scholarly attention in recent years @xcite @xcite . emotion cause extraction (ece), first proposed by @xcite , is a branch of emotion analysis tasks. ece aims at extracting potential causes for given emotions. however, it * corresponding author requires emotions to be marked first, which limits the applications in real-world scenarios. hence, emotion-cause pair extraction (ecpe) @xcite aims to extract all potential pairs of emotions and corresponding causes simultaneously. early methods for ecpe are two-stage models @xcite , which predict emotions and causes first and then filter out wrong pairs from all possible pairs. unfortunately, error propagation happens frequently because the predictions in the first stage directly affect the set of possible pairs in the second stage. to this end, the previous work adopts end-to-end frameworks @xcite @xcite instead of two-stage models. these methods get the representation of emotions and causes separately and then model the pair with them. the distance between the pair of causes is also taken into account because two distant clauses being an emotion-cause pair is usually impossible. with the rapid development of graph convolutional networks @xcite , many methods have started to use graph structures to model the relations between clauses. for instance, rankcp @xcite uses a fully-connected graph to propagate information among clauses. at the same time, integrating a variety of edges while constructing the graph also attracts scholarly attention. currently, the main issue in the field is how to model complex relations with different edges. pairgcn @xcite , for example, demarcates the kinds of edges with the distance between clauses. based on the diverse representation of nodes of pairs and clauses, pbje @xcite divides the edges (e.g., emotion-emotion edges, emotion-cause edges, emotion-pair edges, and so on) through different vertexes. moreover, owing to the relevance between pair extraction, emotion extraction, and cause extraction, most studies adopt multi-task learning to help the model learn a better representation of pairs @xcite @xcite . however, the data of emotions, causes, and pairs are extremely unbalanced, and current methods get their representation using the same graph structure. as shown in figure 1 , most pairs are wrong samples, and only a small number are real emotion-cause pairs. the model can only gain limited knowledge from true pairs because of the small amount, which makes the learning process of ecpe difficult. meanwhile, there is a big difference between the amounts of emotions and causes. an emotion clause can have several causes, while one cause can only lead to one emotion. the data imbalance limits representation layers and the classifiers' learning process and is usually ignored. nearly all of the existing methods regard ecpe as a simple binary classification task and use the same networks (the same encoder, the same graph structure, and so on) to deal with pairs, emotions, and causes, which makes the model unaware of the difference between emotions and causes anywhere except for the labels. consequently, the imbalance has a tremendously adverse effect on the representation of clauses and classifiers' decision boundaries. to sum up, previous models have biased representation of clauses and decision boundaries because they neglect the imbalance of data, which motivated us to propose a joint constrained learning framework with boundary-adjusting for emotion-cause pair extraction (jcb). following the latest study of long-tail data, we focus on the learning process of representation layers and the decision boundaries of classifiers because they prove to be the performance bottlenecks of unbalanced data @xcite . specifically, we first de-sign a joint constrained learning framework enforcing some constraints by converting them into differentiable learning objectives, which generates more useful and learnable samples and alleviates the problem of unbalanced data to some extent. moreover, in order to adjust the narrow decision boundaries, we balance the predicting process by enhancing and correcting results. in summary, the contributions of this paper are as follows: ( 1 2 related work ## experiments we conduct extensive experiments to verify the effectiveness of our proposed model jcb. in this section, we attempt to answer the following questions: rq1: does jcb perform better than existing methods? rq2: are the constrained learning and boundary-adjusted mechanism the key factors affecting the performance? rq3: how do they work in optimization? rq4: how does jcb perform on more unbalanced data?