Document-Level Relation Extraction with Adaptive Thresholding and
Localized Context Pooling
- URL: http://arxiv.org/abs/2010.11304v3
- Date: Wed, 9 Dec 2020 02:25:49 GMT
- Title: Document-Level Relation Extraction with Adaptive Thresholding and
Localized Context Pooling
- Authors: Wenxuan Zhou, Kevin Huang, Tengyu Ma, Jing Huang
- Abstract summary: One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible relations.
We propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems.
- Score: 34.93480801598084
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Document-level relation extraction (RE) poses new challenges compared to its
sentence-level counterpart. One document commonly contains multiple entity
pairs, and one entity pair occurs multiple times in the document associated
with multiple possible relations. In this paper, we propose two novel
techniques, adaptive thresholding and localized context pooling, to solve the
multi-label and multi-entity problems. The adaptive thresholding replaces the
global threshold for multi-label classification in the prior work with a
learnable entities-dependent threshold. The localized context pooling directly
transfers attention from pre-trained language models to locate relevant context
that is useful to decide the relation. We experiment on three document-level RE
benchmark datasets: DocRED, a recently released large-scale RE dataset, and two
datasets CDRand GDA in the biomedical domain. Our ATLOP (Adaptive Thresholding
and Localized cOntext Pooling) model achieves an F1 score of 63.4, and also
significantly outperforms existing models on both CDR and GDA.
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