Improving Sentence-Level Relation Extraction through Curriculum Learning

• URL: http://arxiv.org/abs/2107.09332v1
• Date: Tue, 20 Jul 2021 08:44:40 GMT
• Title: Improving Sentence-Level Relation Extraction through Curriculum Learning
• Authors: Seongsik Park, Harksoo Kim
• Abstract summary: We propose a curriculum learning-based relation extraction model that split data by difficulty and utilize it for learning. In the experiments with the representative sentence-level relation extraction datasets, TACRED and Re-TACRED, the proposed method showed good performances.
• Score: 7.117139527865022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The sentence-level relation extraction mainly aims to classify the relation between two entities in a sentence. The sentence-level relation extraction corpus is often containing data of difficulty for the model to infer or noise data. In this paper, we propose a curriculum learning-based relation extraction model that split data by difficulty and utilize it for learning. In the experiments with the representative sentence-level relation extraction datasets, TACRED and Re-TACRED, the proposed method showed good performances.

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