# Improving Sentence-Level Relation Extraction through Curriculum Learning

arXiv: https://arxiv.org/abs/2107.09332 · HTML: https://mlsanity.com/paper/2107.09332/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [An Improved Baseline for Sentence-level Relation Extraction](https://mlsanity.com/paper/2102.01373/) (2021)
- [Relation Classification with Entity Type Restriction](https://mlsanity.com/paper/2105.08393/) (2021)

## Built on by (most cited first, 8 of 8)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts](https://mlsanity.com/paper/2304.04935/) (2023)
- [About Evaluation of F1 Score for RECENT Relation Extraction System](https://mlsanity.com/paper/2305.09410/) (2023)
- [Noise in Relation Classification Dataset TACRED: Characterization and Reduction](https://mlsanity.com/paper/2311.12298/) (2023)
- [Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction](https://mlsanity.com/paper/2312.11062/) (2023)
- [CPTuning: Contrastive Prompt Tuning for Generative Relation Extraction](https://mlsanity.com/paper/2501.02196/) (2025)
- [Re-Representation in Sentential Relation Extraction with Sequence Routing Algorithm](https://mlsanity.com/paper/2508.21049/) (2025)

## 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.