Towards Realistic Few-Shot Relation Extraction
Sam Brody, Sichao Wu, Adrian Benton · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
In recent years, few-shot models have been applied successfully to a variety of NLP tasks.Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpassed human performance on this task, leading to the impression that few-shot relation classification is solved.In this paper we take a deeper look at the efficacy of strong few-shot classification models in the more common relation extraction setting, and show that typical few-shot evaluation metrics obscure a wide variability in performance across relations.In particular, we find that state of the art few-shot relation classification models overly rely on entity type information, and propose modifications to the training routine to encourage models to better discriminate between relations involving similar entity types.