mlsanityData to 7 Oct 2026

Improving Sentence-Level Relation Extraction through Curriculum Learning

arXiv 2107.09332 ↗First posted 20 Jul 2021cs.CL cs.AIcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 20 Jul 2021.

Builds on

10 of 16 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
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  4. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
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  6. 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
  7. 2019Knowledge Enhanced Contextual Word Representationscited by 294
  8. 2019Matching the Blanks: Distributional Similarity for Relation Learningcited by 271
  9. 2021An Improved Baseline for Sentence-level Relation Extractioncited by 80
  10. 2021Relation Classification with Entity Type Restrictioncited by 31

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Cited by year

Citing papers per year: 2023: 6; 2024: 0; 2025: 2.

8 papers.

  1. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  2. 2023LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunitiescited by 97
  3. 2023Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Promptscited by 1
  4. 2023About Evaluation of F1 Score for RECENT Relation Extraction Systemcited by 1
  5. 2023Noise in Relation Classification Dataset TACRED: Characterization and Reductioncited by 0
  6. 2023Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extractioncited by 0
  7. 2025CPTuning: Contrastive Prompt Tuning for Generative Relation Extractioncited by 0
  8. 2025Re-Representation in Sentential Relation Extraction with Sequence Routing Algorithmcited by 0