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2022 NAACL NAACL 2022

Few-Shot Document-Level Relation Extraction

Abstract

AbstractWe 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 (https://github.com/nicpopovic/FREDo).

🌉 Interdisciplinary Bridge — Knowledge & Reasoning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — none-of-the-above prediction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Nicholas Popovic , Michael Färber

Topics

Natural Language Processing > Applications > Information Extraction Knowledge & Reasoning > Representation > Knowledge Graphs Machine Learning > Learning Paradigms > Few-Shot Learning Machine Learning > Learning Types > Few-Shot Learning

Keywords

few-shot learning domain adaptation information extraction knowledge graph document-level relation extraction none-of-the-above prediction
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