Sentence-level relation extraction mainly aims to classify the relation between two entities in a sentence. The sentence-level relation extraction corpus often contains data that are difficult for the model to infer or noise data. In this paper, we propose a curriculum learning-based relation extraction model that splits data by difficulty and utilizes them for learning. In the experiments with the representative sentence-level relation extraction datasets, TACRED and Re-TACRED, the proposed method obtained an F1-score of 75.0% and 91.4% respectively, which are the state-of-the-art performance.
@article{arxiv.2107.09332,
title = {Improving Sentence-Level Relation Extraction through Curriculum Learning},
author = {Seongsik Park and Harksoo Kim},
journal= {arXiv preprint arXiv:2107.09332},
year = {2021}
}