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Few-Shot Document-Level Relation Extraction

Few-Shot Document-Level Relation Extraction

Published: 2022

Buchtitel: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Verlag: Association for Computational Linguistics

Referierte Veröffentlichung


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-of-the-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 (

Download: Media:FSDLRE.pdf


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Text Mining, Informationsextraktion, Natürliche Sprachverarbeitung, Deep Learning