  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.
