End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network (2020)

Abstract: Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that most emotions usually have few causes mentioned in their contexts, we present a novel end-to-end Pair Graph Convolutional Network (PairGCN) to model pair-level contexts so that to capture the dependency information among local neighborhood candidate pairs. Moreover, in the graphical network, contexts are grouped into three types and each type of contexts is propagated by its own way. Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.

## introduction emotion-cause pair extraction (ecpe), which was first proposed in @xcite , aims to extract emotion expressions and their corresponding causes in a document simultaneously. different from emotion cause extraction (ece) @xcite which extracts the causes for given emotion expressions, epce is a much more challenging task. there has been a surging interest in developing neural models either for emotion cause extraction or for emotion extraction, while ecpe, as a special causal relation extraction task, is newly proposed and remains largely unexplored. previous research on ecpe @xcite focused on designing pipeline systems in which emotion clauses and cause clauses are extracted separately, and the two sets of clauses are paired to generate candidate emotion-cause pairs, and then emotion-cause pairs are selected from these candidate pairs with a filter. hence, prediction errors unavoidably accumulate through the pipeline framework. therefore, in this work, we aim to design an end-to-end framework in which any two clauses in a document are paired (i.e., one is a candidate emotion clause, and the other is a candidate cause clause) so as to generate candidate emotion-cause pairs and then emotion-cause pairs are selected from these candidate pairs. e.g., in fig. 1 , there are 25 candidate pairs, and only (c 4 , c 2 ) and (c 4 , c 3 ) are emotion-cause pairs. furthermore, modelling contextual information for a candidate pair is also crucial for ecpe. in previous pipeline systems @xcite , two features were extracted for each clause in a document for emotion extraction and emotion cause extraction respectively, and then a candidate pair was represented by the combination of these clause-level features. however, dependency among candidate pairs does not take into account. in fact, an emotion usually has a few cause clauses occurring within a specific distance from the emotion expression. e.g, in the chinese ecpe corpus provided by @xcite , ∼90% emotions has one and only one cause clause, and ∼96% cause clauses occur within a window size of 2 from their corresponding emotion clauses. the emotion-cause co-occurrence property indicates that in a local neighborhood if one candidate pair has been detected as an emotion-cause pair, other candidate pairs are usually non-emotion-cause pairs. thus, modelling contextual information should consider pairlevel dependency. here, a local neighborhood refers to a set of candidate pairs whose candidate emotion clauses are the same, and candidate cause clauses are not far away from each other. in this paper, we propose a novel pair graph convolutional network (pairgcn), an end-to-end model for ecpe. we first construct a pair graph to model three types of dependency relations among the candidate pairs in a local neighborhood, where a node represents a candidate pair and an edge connecting two nodes represents a dependency relation between the corresponding two candidate pairs. then, a graph convolutional network (gcn) is designed to use the three types of edges to propagate contextual information in the pair graph. above all, our main contributions can be summarized as follow: ## related works in this section, we will briefly summarise related research on emotion cause extraction (ece), emotioncause pair extraction (ecpe), and graph neural networks (gnns). ## methodology in this section, we briefly introduce the definition of ecpe. then, we describe our pairgcn which models two types of contexts for ecpe: sequential clause context and pair-level context. the former refers to the clause sequence in a given document that provides sequential information for each clause, and the latter refers to the candidate pairs in a local neighborhood which gives dependency information for each candidate pair. accordingly, as illustrated in fig. 2 , there are two encoders in our pairgcn: clause-level context encoder and pair-level context encoder. the clause-level context encoder uses two hierarchical bilstm networks to model the sequential clause context and then extracts an emotion feature and a cause feature for a clause respectively. the pair-level context encoder uses a pair graph convolutional network to model the pair-level context and then extracts a contextual feature for a candidate pair. finally, classification assigns a label to a candidate pair according to its feature representation.