# An End-to-End Mutually Interactive Emotion-Cause Pair Extractor via Soft Sharing
###### Abstract  
Emotion-cause pair extraction (ECPE), i.e., extracting pairs of emotions and corresponding causes from text, has recently attracted a lot of research interest. However, current ECPE models face two problems: (1) The common two-stage pipeline causes the error to be accumulated. (2) Ignoring the mutual connection between the extraction and pairing of emotion and cause limits the performance. In this paper, we propose a novel end-to-end mutually interactive emotion-cause pair extractor (Emiece) that is able to effectively extract emotion-cause pairs from all potential clause pairs. Specifically, we design two soft-shared clause-level encoders in an end-to-end deep model to measure the weighted probability of being a potential emotion-cause pair. Experiments on standard ECPE datasets show that Emiece achieves drastic improvements over the original two-step ECPE model and other end-to-end models  
(clause 7-clause 1) and (clause 8-clause 1), i.e., the emotion clause \"[PERSON] threw a tantrum\" (Figure 1) and its corresponding cause clause \"[PERSON] assigned No.1 to [PERSON] and No.2 to [PERSON]\" (Figure 1), and the emotion clause \"even cried\" (Figure 1) and its corresponding cause clause \"[PERSON] assigned No.1 to [PERSON] and No.2 to [PERSON]\" (Figure 1), without relying on the emotion annotations \"_anger_\" and \"_sadness_\" in the clause. The matching of two different emotion clauses with one identical cause clause, or the pairing of one emotion clause with two different cause clauses, as in the example in [12], reflects the increased attention of the ECPE task to the connection between the emotion and cause. Currently, ECPE methods can be divided into two categories: two-step ECPE and end-to-end ECPE. The two-step ECPE [12] is composed by the emotion extraction and cause extraction step and the pairing and filtering step. As it is not an end-to-end model, the accumulated error of the first step will affect the result in the second step. End-to-end models [13,14] consider the mutual interaction between emotion and cause. However, the process of mutual interaction is simply implemented by transferring information from one decoder to another uniaxially, thus harming the mutual transfer of information. Moreover, some of the end-to-end models of [14,15] contain a huge number of parameters, resulting in an excessive training time and unremarkable model performance. In this paper, our target is to effectively extract emotion-cause pairs from all potential clause pairs with fewer parameters. To address the aforementioned challenges, we propose an end-to-end model, Emiece, that predicts the emotion-cause pair from the raw document. We observe that ECPE can be viewed as three mutually related tasks: a primary task of predicting pairings and two auxiliary tasks of predicting emotion and cause clauses respectively. To efficiently learn the three tasks, we consider adopting multi-task learning to establish the connection of them. Multi-task learning [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28] is an effective way to achieve better generalization performance with a group of related tasks, while sharing some common parameters. Since the two auxiliary tasks are highly similar, we attempt to utilize multi-task learning to tackle the problem of ECPE models' unsatisfactory performance. Inspired by [29], we choose to leverage a soft-sharing approach to exploit the intrinsic connection between emotion and cause. After the word-level encoder, each clause is passed through the emotion and cause clause probabilities that indicate the importance of the clause in terms of emotion and cause, and then we obtain the weighted representation of emotion clause and cause clause, respectively. The aim of the weighted representation is to make the true representation and the false one as separate as possible in the feature space so that the extracted representation  
Figure 1: The difference between ECE tasks and ECPE tasks.