Abstract

Relation extraction is an important task in structuring content of text data, and becomes especially challenging when learning with weak supervision-where only a limited number of labeled sentences are given and a large number of unlabeled sentences are available. Most existing work exploits unlabeled data based on the ideas of self-training (i.e., bootstrapping a model) and self-ensembling (e.g., ensembling multiple model variants). However, these methods either suffer from the issue of semantic drift, or do not fully capture the problem characteristics of relation extraction. In this paper, we leverage a key insight that retrieving sentences expressing a relation is a dual task of predicting the relation label for a given sentence-two tasks are complementary to each other and can be optimized jointly for mutual enhancement. To model this intuition, we propose DualRE, a principled framework that introduces a retrieval …

Date

2019

Authors

Hongtao Lin, Jun Yan, Meng Qu, Xiang Ren

Book

The world wide web conference

Pages

1073-1083