Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction

TL;DR

This paper introduces MGSAG, a graph-based model that captures multi-granularity semantic features to improve emotion-cause pair extraction, especially in position-insensitive scenarios, outperforming existing methods.

Contribution

The paper proposes a novel Multi-Granularity Semantic Aware Graph model that jointly models fine- and coarse-grained semantic relations without distance constraints, enhancing ECPE performance.

Findings

MGSAG surpasses state-of-the-art ECPE models.

Significant improvement on position-insensitive data.

Effective integration of semantic dependencies and clause relations.

Abstract

The Emotion-Cause Pair Extraction (ECPE) task aims to extract emotions and causes as pairs from documents. We observe that the relative distance distribution of emotions and causes is extremely imbalanced in the typical ECPE dataset. Existing methods have set a fixed size window to capture relations between neighboring clauses. However, they neglect the effective semantic connections between distant clauses, leading to poor generalization ability towards position-insensitive data. To alleviate the problem, we propose a novel Multi-Granularity Semantic Aware Graph model (MGSAG) to incorporate fine-grained and coarse-grained semantic features jointly, without regard to distance limitation. In particular, we first explore semantic dependencies between clauses and keywords extracted from the document that convey fine-grained semantic features, obtaining keywords enhanced clause representations. Besides, a clause graph is also established to model coarse-grained semantic relations between clauses. Experimental results indicate that MGSAG surpasses the existing state-of-the-art ECPE models. Especially, MGSAG outperforms other models significantly in the condition of position-insensitive data.