MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction

• URL: http://arxiv.org/abs/2109.04108v1
• Date: Thu, 9 Sep 2021 09:02:23 GMT
• Title: MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction
• Authors: Manqing Dong, Chunguang Pan, and Zhipeng Luo
• Abstract summary: We propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction. We show that incorporating the above two types of mapping information in both pretraining and fine-tuning can significantly improve the model performance.
• Score: 11.821464352959454
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples. Recent works try leveraging the advance in few-shot learning to solve the low resource problem, where they train label-agnostic models to directly compare the semantic similarities among context sentences in the embedding space. However, the label-aware information, i.e., the relation label that contains the semantic knowledge of the relation itself, is often neglected for prediction. In this work, we propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction. We show that incorporating the above two types of mapping information in both pretraining and fine-tuning can significantly improve the model performance on low-resource relation extraction tasks.

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