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Improving Sentence-Level Relation Extraction through Curriculum Learning

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arxiv 2107.09332 v2 pith:MNST4BPE submitted 2021-07-20 cs.CL cs.AI

Seongsik Park , Harksoo Kim This is my paper · ORCID
classification cs.CLcs.AI
keywords relationextractionsentence-leveldatacurriculumlearningmodelaims
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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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Cited by 2 Pith papers

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  1. Re-Representation in Sentential Relation Extraction with Sequence Routing Algorithm

    cs.CL 2025-08 conditional novelty 6.0 of 10

    A sequence-routing capsule head over RoBERTa/BERT embeddings achieves strong relation extraction scores on TACRED, ReTACRED, TACRED-Rev, and Conll04, while the paper interprets the performance as evidence of re-repres...

  2. CPTuning: Contrastive Prompt Tuning for Generative Relation Extraction

    cs.CL 2025-01 conditional novelty 5.0 of 10

    CPTuning adds contrastive learning and label smoothing to generative relation extraction, letting a T5 model extract zero, one, or multiple relations per entity pair with Trie-constrained decoding.

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