Mention-centered Graph Neural Network for Document-level Relation Extraction
Abstract
Document-level relation extraction aims to discover relations between entities across a whole document. How to build the dependency of entities from different sentences in a document remains to be a great challenge. Current approaches either leverage syntactic trees to construct document-level graphs or aggregate inference information from different sentences. In this paper, we build cross-sentence dependencies by inferring compositional relations between inter-sentence mentions. Adopting aggressive linking strategy, intermediate relations are reasoned on the document-level graphs by mention convolution. We further notice the generalization problem of NA instances, which is caused by incomplete annotation and worsened by fully-connected mention pairs. An improved ranking loss is proposed to attend this problem. Experiments show the connections between different mentions are crucial to document-level relation extraction, which enables the model to extract more meaningful higher-level compositional relations.
Cite
@article{arxiv.2103.08200,
title = {Mention-centered Graph Neural Network for Document-level Relation Extraction},
author = {Jiaxin Pan and Min Peng and Yiyan Zhang},
journal= {arXiv preprint arXiv:2103.08200},
year = {2021}
}