ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning
Abstract
Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks. However, conventional pre-training objectives do not explicitly model relational facts in text, which are crucial for textual understanding. To address this issue, we propose a novel contrastive learning framework ERICA to obtain a deep understanding of the entities and their relations in text. Specifically, we define two novel pre-training tasks to better understand entities and relations: (1) the entity discrimination task to distinguish which tail entity can be inferred by the given head entity and relation; (2) the relation discrimination task to distinguish whether two relations are close or not semantically, which involves complex relational reasoning. Experimental results demonstrate that ERICA can improve typical PLMs (BERT and RoBERTa) on several language understanding tasks, including relation extraction, entity typing and question answering, especially under low-resource settings.
Cite
@article{arxiv.2012.15022,
title = {ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning},
author = {Yujia Qin and Yankai Lin and Ryuichi Takanobu and Zhiyuan Liu and Peng Li and Heng Ji and Minlie Huang and Maosong Sun and Jie Zhou},
journal= {arXiv preprint arXiv:2012.15022},
year = {2021}
}
Comments
Accepted by ACL-IJCNLP 2021 main conference