Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction (2020)

Abstract: Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. Previous work employs two-step approaches, in which the first step extracts emotion clauses and cause clauses separately, and the second step trains a classifier to filter out negative pairs. However, such pipeline-style system for emotion-cause pair extraction is suboptimal because it suffers from error propagation and the two steps may not adapt to each other well. In this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. It models the interrelations between the clauses in a document to learn clause representations with graph attention, and enhances clause pair representations with kernel-based relative position embedding for effective ranking. Experimental results show that our approach significantly outperforms the current two-step systems, especially in the condition of extracting multiple pairs in one document.

## introduction emotion cause analysis has attracted increasing research attention in sentiment analysis and text mining community in recent years @xcite @xcite @xcite . its goal is to detect causes or stimuli for a certain emotion expressed in text. understanding why an emotion occurs has broad applications such as consumer review mining and public opinion monitoring. previous studies mostly focus on emotion cause extraction task which aims to identify cause(s) for a given emotion. @xcite pointed out that this setting ignores the mutual indication of emotions and causes, and the need of emotion anno-tation in advance restricts the range of applications. to overcome such limitations, they put forward a new research task named emotion-cause pair extraction, aiming to extract all emotion expression clauses coupled with their causes from a given document. as shown in the following example, an emotion clause c 3 and its corresponding cause clause c 2 construct an emotion-cause pair (c 3 , c 2 ): example. he told us that since his illness (c 1 ), his classmates and advisors have given him much help about the schoolwork (c 2 ). he has been touched (c 3 ), and said that he will repay them (c 4 ). compared with emotion cause extraction, emotion-cause pair extraction is a more challenging task, because we need a comprehensive understanding of document content and structure to perform emotion-cause co-extraction and discriminate emotion-cause clause pairs from negative ones. @xcite proposed to tackle emotioncause pair extraction using a two-step solution. at the first step, a multi-task lstm network extracts emotion clauses and cause clauses separately. then at the second step, a binary classifier is used to filter out negative pairs from all possible pairs. although the two-step solution has shown its effectiveness, such pipeline-style system is suboptimal for emotion-cause pair extraction, because it is confronted with error propagation, and the two steps may not adapt to each other well. coherent document has an underlying structure @xcite and there is a causal relationship between the two clauses of an emotion-cause pair, which distinguishes it from other non-emotion-cause pairs in the document. thus, knowledge about the interrelations between the clauses in a document is beneficial for extracting potential emotion-cause pairs. further, according to the cohesion and coherence of discourse @xcite , the probability of two distant clauses containing causal relationship is relatively small. thus, relative position information between two clauses of a clause pair can be considered as an effective feature for emotion-cause pair extraction. based on the above two considerations, in this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a given document, and propose a one-step approach which emphasizes inter-clause modeling to perform end-to-end extraction. our approach first models the inter-clause relationships via exploiting graph attention to learn clause representations, facilitating pair extraction through capturing the latent relationship between two clauses. it then learns clause pair representations and rank these pairs to extract emotion-cause pairs. a kernelbased relative position embedding scheme is proposed to model the mutual impact among relative positions and enhance clause pair representations for effective ranking. we integrate the two components into a unified neural network, which is optimized end-to-end. unlike the previous two-step solution, our approach can directly extract emotioncause pairs from documents. the main contributions of this work are summarized as follows. ## problem formulation given a document @xmath0, c 2 , . . . , c |d| ) where |d| is the number of clauses and the i-th clause c @xmath1, w i 2 , . . . , w i |c i | ) is a word sequence, our goal is to extract all emotion-cause pairs in d: @xmath2, c cau 1 ), (c emo 2 , c cau 2 ), . . .} , (1) where (c emo j , c cau j ) is the j-th pair, c emo @xmath3, and c cau @xmath4. note that an emotion may have more than one cause, and the same cause may also become the stimulus of multiple emotions. ## proposed approach we propose a one-step approach named rankcp, which ranks clause pair candidates in a document to extract emotion-cause pairs. the overall architecture is shown in fig. 1 , which consists of three components. the first component learns vector representations of clauses in a given document. the second component models the relationships between clauses to obtain better clause representations. the third component learns clause pair representations enhanced with relative position modeling, and ranks clause pair candidates to extract emotion-cause pairs. ## experiments we conduct extensive experiments to verify the effectiveness of our proposed model rankcp.