MGCL : Multi-Granularity Clue Learning for Emotion-Cause Pair Extraction via Cross-Grained Knowledge Distillation (2024)

Abstract: Emotion-cause pair extraction (ECPE) aims to identify emotion clauses and their corresponding cause clauses within a document. Traditional methods often rely on coarse-grained clause-level annotations, which can overlook valuable fine-grained clues. To address this issue, we propose Multi-Granularity Clue Learning (MGCL), a novel approach designed to capture fine-grained emotion-cause clues from a weakly-supervised perspective efficiently. In MGCL, a teacher model is leveraged to give sub-clause clues without needing fine-grained annotated labels and guides a student model to identify clause-level emotion-cause pairs. Furthermore, we explore domain-invariant extra-clause clues under the teacher model’s advice to enhance the learning process. Experimental results on the benchmark dataset demonstrate that our method achieves state-of-the-art performance while offering improved interpretability.

## introduction investigating the underlying causes behind emotions is a promising research direction in sentiment analysis. the task of emotion-cause pair extraction (ecpe) @xcite involves extracting all pairs of emotions and their corresponding causes within a document. compared to the emotion cause extraction (ece) task first proposed by @xcite , ecpe is significantly more challenging because the emotions in the document do not need to be pre-annotated. in other words, this task requires the simultaneous identification of all potential pairs, presenting a new challenge for document understanding capabilities. traditional ecpe methods can be categorized into two paradigms: two-stage methods and endto-end methods. @xcite proposed a * corresponding author. ## problem formulation given a document @xmath0, c 2 , . . . , c |d| ) consisting of |d| clauses, where i-th clause c @xmath1, w i 2 , . . . , w i |c i | ) contains |c i | words, the goal of ecpe is to extract all the emotion-cause pairs from the document d: @xmath2. . . , (c e i , c c j ), . . .} (1 ≤ i, @xmath3, and c c j represents the corresponding cause clause in the pair. meanwhile, ecpe involves two auxiliary tasks: clause extraction (ee) and cause clause extraction (ce). a clause c i is an emotion clause if any pair (c e i , c c j ) is established. this can be formally defined as follows: ## conclusion in this paper, we proposed a multi-granularity clue learning (mgcl) method, which efficiently captures multi-granularity emotion-cause clues. from a weakly-supervised perspective, we introduce cross-grained knowledge distillation to learn fine-grained knowledge from coarse-grained annotations. the remarkable performance on the benchmark corpus demonstrates fine-grained clues can significantly assist in performing the ecpe task.