Improving Sentence-Level Relation Extraction through Curriculum Learning

20 Jul 2021  ·  Seongsik Park, Harksoo Kim ·

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.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Relation Extraction Re-TACRED EXOBRAIN F1 91.4 # 1
Relation Extraction TACRED EXOBRAIN F1 75.0 # 2

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