Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme (2020)

Abstract: The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts. Most recent studies are based on the likelihood of Cartesian product among all clause candidates, resulting in a high computational cost. Targeting this issue, we regard the task as a sequence labeling problem and propose a novel tagging scheme with coding the distance between linked components into the tags, so that emotions and the corresponding causes can be extracted simultaneously. Accordingly, an end-to-end model is presented to process the input texts from left to right, always with linear time complexity, leading to a speed up. Experimental results show that our proposed model achieves the best performance, outperforming the state-of-the-art method by 2.26% (p<0.001) in F1 measure.

## conclusion in this paper, we consider the emotion-cause pair extraction as a sequence labeling problem and propose an end-to-end model based on a novel tagging scheme with multiple labels. the proposed model is capable of integrating the emotion-cause structure into a unified framework, so that emotions with the related causes can be extracted simultaneously. moreover, the proposed model parses the input texts in order from left to right, greatly reducing the search space, leading to a speed up. experimental results demonstrate the effectiveness and robustness of the proposed method on a benchmark dataset. in the future, we will explore the extension of this approach to achieve full coverage.