Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction (2022)

Abstract: Emotion-cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses, which have recently received growing attention. Previous methods sequentially encode features with a specified order. They first encode the emotion and cause features for clause extraction and then combine them for pair extraction. This lead to an imbalance in inter-task feature interaction where features extracted later have no direct contact with the former. To address this issue, we propose a novel **P**air-**B**ased **J**oint **E**ncoding (**PBJE**) network, which generates pairs and clauses features simultaneously in a joint feature encoding manner to model the causal relationship in clauses. PBJE can balance the information flow among emotion clauses, cause clauses and pairs. From a multi-relational perspective, we construct a heterogeneous undirected graph and apply the Relational Graph Convolutional Network (RGCN) to capture the multiplex relationship between clauses and the relationship between pairs and clauses. Experimental results show that PBJE achieves state-of-the-art performance on the Chinese benchmark corpus.

## introduction emotion cause extraction (ece) is a kind of emotion analysis task which is first proposed by @xcite and has developed for a long time @xcite @xcite . ece extracts the cause for the input document and certain emotion labels. however, emotions in the documents need to be annotated in advance, which requires manual input and takes lots of time @xcite . hence, @xcite proposes a new task called emotion-cause pair extraction (ecpe). given a document as the input, ecpe extracts the clauses which express emotions and their corresponding clauses which express causes (as shown in figure 1 ). intuitively, ecpe is much more challenging because the clauses classification task and the pairs matching task need to be completed simultaneously. for ecpe, @xcite first proposes a two-stage method. however, the two-stage method may cause the problem of error propagation. to solve this problem, the previous work uses end-toend methods @xcite @xcite . most of them use sequential encoding, in which their task-specific features are learned sequentially in a predefined order. specifically, following @xcite , ecpe contains two auxiliary tasks, which are emotion clause extraction (ee) and cause clause extraction (ce). the previous work first separately models the clauses for ee and ce. then they use the clause representations for emotion and cause clauses to model the pairs for ecpe. however, the sequential encoding only considers the intra-relationship within pairs or clauses while ignoring the inter-relationship between them. in the sequential encoding, the information can only flow from emotion/cause clause encoder to pair encoder but not vice versa, resulting in the exposure of different amounts of information to pair encoder and clause encoders @xcite . in this way, if the emotion/cause clause encoder makes incorrect predictions, it will severely misguide the predictions of pair. for example, in the figure 1 , the previous model rankcp @xcite wrongly extracts the c 11 as an emotion clause and the c 10 as a cause clause with high degree of confidence (about 0.98 for c 11 and 0.94 for c 10 ). finally, the pair encoder of rankcp extracts the couple (c 11 , c 10 ) as a candidate pair, which is an incorrect answer. the imbalanced information flow (i.e., from clauses to pairs) might have induced this error. on the contrary, the joint encoding manner is getting more attention in multi-task learning @xcite @xcite . it can not only balance the information flow among emotion clauses, cause clauses and pairs to deal with the problems of sequential encoding we mentioned above but also take into account the causal relationship @xcite between clauses in ecpe. since the joint encoding can make the emotion/cause clause encoder and pair encoder interact with each other. during the process of encoding, the clause encoders can pay more attention to whether a clause is suitable for pairs rather than only focus on the information about emotion or cause. the causal relationship is a decisive factor in judging whether emotions and causes match. for example, in figure 1 , c 6 and c 13 both express anger, and c 12 is cause clause. however, (c 13 , c 12 ) is a pair and (c 6 , c 12 ) is not. if we separately model the pairs and clauses, the lack of relationship information between these two clauses will increase the difficulty for the model to judge this situation. given the above mentioned situation, we propose a novel pair-based joint encoding (pbje) method, which simultaneously generates pairs and clauses features in a joint feature encoding manner. specifically, we model the inter-relationship between pairs and clauses, in which a pair only interacts with the corresponding clauses. it helps pairs learn representations and model the causal relationship from clauses. meanwhile, the key information about emotion and cause clauses is different. therefore, different features should be extracted from these two clauses. considering these multiplex relationships, we construct a heterogeneous undirected graph and apply relational graph convolutional networks (rgcn) @xcite on it, which includes four kinds of nodes and five kinds of edges, utilizing different approaches to connect the nodes. thus, it can make the information flow between emotion clauses, between emotion clauses and pairs, etc., more efficient. ## task definition given a document @xmath0, c 2 , . . . , c n ) of n clauses and the i-th clauses c @xmath1, w i 2 , . . . , w i m ) of m words, ecpe task aims to extract all the emotion-cause pairs in d: meanwhile, ecpe has two auxiliary tasks which are emotion clauses extraction (ee) and cause clauses extraction (ce). a clause c i is emotion clause if any pair (c i , c j ) is established. it can be defined as follow: ## proposed model in this section, we mainly describe our method, which encodes the pairs and clauses simultaneously and models the causal relationship from clauses in relational graph convolutional network (rgcn). the structure of pbje is shown in figure 2 . ## experiments extensive experiments are conducted to verify the effectiveness of the pbje. ## conclusion in this paper, we propose a novel pair-based joint encoding (pbje) network, which encodes the pairs and clauses features simultaneously. it can balance the inter-task feature interaction compared with sequential encoding and model the causal relationship between emotion clauses and corresponding cause clauses by pairs. furthermore, it can avoid the wrong predictions in previous tasks in the multi-task learning. from a multi-relational perspective, we propose a relational graph convolutional network (rgcn) framework to capture the relationship among emotion clauses, cause clauses, pairs, and document, including four types of node and five types of edge. the experiments on the chinese benchmark corpus show that pbje achieves state-of-the-art performance.