# An End-to-End Mutually Interactive Emotion-Cause Pair Extractor via Soft Sharing
## 2 Related Work  
The emotion-cause extraction (ECE) task was first proposed in [1]. As a word-level task, the extraction is fulfilled with traditional machine learning and rule-based approaches [5; 6; 7; 8; 9; 10; 11; 31; 32; 33; 34]. For example, in [7], the authors proposed a fine-grained rule-based method for the task and conducted experiments on the Chinese microblog posts corpus labeled by human annotators. Despite the overall performance of the word-level task not being promising enough, it provides a new way to look at the emotion classification task.  
Another kind of emotion-cause extraction task is based on a clause that solves the problem of word-level labeling in previous work [35; 36; 37; 38; 39]. In [35], the authors employed a multi-kernel learning method for the clause-level task on a Chinese emotion cause corpus. Moreover, with the development of deep learning, multiple recurrent neural networks (RNNs) related models have been proposed to solve clause-level tasks due to their excellent performance in analyzing the relationship between different sequences [40; 41]. Long short-term memory (LSTM), an advanced version of RNNs, achieves better performance in related tasks thanks to its forgetting mechanism [38]. Although clause-level methods relax word-level annotations into clause-level annotations and achieve higher performance due to the development of neural networks, it is still restricted by manual annotations. In addition, it neglects the mutual relationship and interaction between emotion and cause [42].  
To overcome the mentioned drawbacks of ECE, [PERSON]. [12] proposed emotion-cause pair extraction (ECPE) and they conducted a two-step hierarchical structure network for the task. This model separates the emotion and cause extraction and the pairing into two steps; therefore, the mistakes made in the first step will affect the results of the second step.  
To solve these limitations in [12], several end-to-end ECPE models have been proposed [13; 14; 15; 43]. [PERSON]. [14] proposed to model interactions in emotion-cause pairs by means of a two-dimensional transformer [44], which in turn represents emotion-cause pairs in a 2D form. Then a joint framework [45] is used to integrate the two-dimensional representation, interaction and prediction. The work of [15] introduces multi-label learning (MLL) in the ECPE task. The emotion clause and cause clause are designated as the center of the multi-label learning window [46], respectively, and the window slides as the center position is moved. The two joint parts are integrated to obtain the ultimate result. These two models achieved state-of-the-art performance in the ECPE task. Nonetheless, the enormous amount of parameters makes the training overhead of both models extremely large. After that, the method of Ref. [13] uses Bi-LSTM to perform word-level embedding for the input clauses and encodes representations for the emotion and reason clauses.  
Finally, a layer of fully connected network is used to predict the matching pairs. It achieves comparable performance with fewer parameters and a simpler architecture. However, its unsatisfactory performance suggests that it does not fully exploit the intrinsic connection between sentiment and cause.