Paper ID: 2406.10432

AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction

Peitao Han, Lis Kanashiro Pereira, Fei Cheng, Wan Jou She, Eiji Aramaki

Existing in-context learning (ICL) methods for relation extraction (RE) often prioritize language similarity over structural similarity, which can lead to overlooking entity relationships. To address this, we propose an AMR-enhanced retrieval-based ICL method for RE. Our model retrieves in-context examples based on semantic structure similarity between task inputs and training samples. Evaluations on four standard English RE datasets show that our model outperforms baselines in the unsupervised setting across all datasets. In the supervised setting, it achieves state-of-the-art results on three datasets and competitive results on the fourth.

Submitted: Jun 14, 2024