Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion Cause Pair Extraction (2022)

Abstract: The emotion cause pair extraction (ECPE) task aims to extract emotions and causes as pairs from documents. We observe that the relative distance distribution of emotions and causes is extremely imbalanced in the typical ECPE dataset. Existing methods have set a fixed size window to capture relations between neighboring clauses. However, they neglect the effective semantic connections between distant clauses, leading to poor generalization ability towards position-insensitive data. To alleviate the problem, we propose a novel \textbf{M} ulti- \textbf{G} ranularity \textbf{S} emantic \textbf{A} ware \textbf{G} raph model (MGSAG) to incorporate fine-grained and coarse-grained semantic features jointly, without regard to distance limitation. In particular, we first explore semantic dependencies between clauses and keywords extracted from the document that convey fine-grained semantic features, obtaining keywords enhanced clause representations. Besides, a clause graph is also established to model coarse-grained semantic relations between clauses. Experimental results indicate that MGSAG surpasses the existing state-of-the-art ECPE models. Especially, MGSAG outperforms other models significantly in the condition of position-insensitive data.

## introduction emotion cause analysis (eca) has attracted increasing research interest in recent years @xcite @xcite , because of the great potential of applying in consumer review mining, public opinion monitoring, and online empathetic chatbot building. its goal is to detect causes or stimuli for a certain emotion expressed in text. emotion cause pair extraction (ecpe) @xcite is a new task related to eca, which is concerned with causal relationships be-figure 1 : the distribution of the relative distance of an emotion clause and a cause clause that comprise a pair in the ecpe dataset @xcite . dist0, dist1, and dist2 mean the relative distances between the two clauses are 0, 1, and 2 respectively. @xmath0. ## related work according to whether the relative position information is used explicitly or not, existing ecpe works can be divided into two categories: positionsensitive approaches and position-insensitive approaches. position-sensitive approaches. most methods @xcite @xcite have set a fixed size window to reduce the number of candidate pairs according to the inherent position bias in the dataset, because of the sparsity of true emotion-cause pairs compared with candidate emotion-cause pairs. besides, chen et al. (2020b) leveraged the relative position information explicitly in the process of pair representation learning. the ecpe-mll model proposed by ding et al. (2020b) is the state-of-the-art method in the ecpe task. an over-reliance on relative position information makes these methods have poor generalization ability towards position-insensitive data. position-insensitive approaches. some sequencebased methods without relative position information (xia and ding, 2019; chen et al., 2020a; fan et al., 2020) seem to be position-insensitive. xia and ding (2019) proposed a rnn-based framework and generate candidate pairs by applying the cartesian product. chen et al. (2020a) reformulated the ecpe task as a unified sequence labeling problem. fan et al. (2020) modeled the extraction of emotion-cause pairs as performing a sequence of transitions and actions. however, these methods have shown poor performance on positioninsensitive data due to the neglect of effective semantic connections between distant clauses. different from the above methods, our model incorporates fine-grained and coarse-grained semantic features jointly, which can alleviate the position bias problem well. ## problem formulation given a document @xmath1, c 2 , ..., c |d| } where |d| is the number of clauses, the clauses are formed into |d| × |d| candidate emotion-cause pairs using cartesian product: @xmath2..., (c e i , c c j ), ...}, where c e i is clause c i serving as a candidate emotion clause, c c j is clause c j serving as a candidate cause clause. the ecpe task is to assign a binary label to each candidate pair (c e i , c c j ), where "1" means that clause c i is an emotion clause and clause c j provides the cause of it, otherwise "0". ## methodology we propose a multi-granularity semantic aware graph model to alleviate the position bias problem in ecpe. more concretely, we obtain fine-grained semantic aware clause representations based on a clause-keyword bipartite graph. simultaneously, coarse-grained semantic aware clause representations are generated based on a fully connected clause graph. as shown in figure 2 , the model consists of four components: 1) document encoding, 2) fine-grained semantic aware graph (fgsag), 3) coarse-grained semantic aware graph (cgsag), 4) pair classification. ## experiments we conduct a series of experiments to verify the effectiveness of mgsag. ## conclusion and future work in this paper, we propose mgsag to alleviate the position bias problem in the ecpe task. our approach implements clause representation learning via fine-grained semantics introduced by keywords and coarse-grained semantics among clauses. experimental results show that mgsag surpasses the state-of-the-art baselines, and outperforms other methods significantly on the position-insensitive data. in the future, we would like to tackle the problem of imbalanced data by reducing non-emotioncause pairs, based on a position-insensitive approach.