M ap RE : An Effective Semantic Mapping Approach for Low-resource Relation Extraction (2021)

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.

## introduction relation extraction (re), which aims at discovering the correct relation between two entities in a given sentence, is a fundamental task in nlp @xcite . the problem is generally regarded as a supervised classification problem by training on large-scale labelled data @xcite . neural models, e.g. rnn-based methods @xcite , or more recently, bert-based methods @xcite , have shown promising results on re tasks, where they achieve state-of-the-art performance or even comparable with human performance on several public re benchmarks. despite the promising performance of the existing neural relation classification frameworks, recent studies @xcite found that the model performance drops dramatically as the number of instances for a relation decreases, e.g., for long-tail ## conclusion in this work, we propose mapre, a semantic mapping approach considering both label-agnostic and label-aware information for low-resource relation extraction (re). extensive experiments on lowresource supervised re, few-shot re, and zeroshot re tasks present the outstanding performance of the proposed framework. the results suggest the importance of both label-agnostic and labelaware information in pretraining and fine-tuning the model for low-resource re tasks. in this work, we did not investigate the potential effect caused by the domain shift problem, and we will leave the analysis on this to future works.