Few-Shot Document-Level Relation Extraction
Nicholas Popovič, Michael Färber · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark.As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora provide more realism, particularly regarding none-ofthe-above (NOTA) distributions.Therefore, we propose a set of FSDLRE tasks and construct a benchmark based on two existing supervised learning data sets, DocRED and sciERC.We adapt the state-of-the-art sentence-level method MNAV to the document-level and develop it further for improved domain adaptation.We find FSDLRE to be a challenging setting with interesting new characteristics such as the ability to sample NOTA instances from the support set.The data, code, and trained models are available online 1 .