Mitigating Bias in Automated Essay Scoring for ESL Learners via Contrastive Learning
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Abstract (arXiv)
Automated Essay Scoring systems disproportionately penalize high-proficiency English as a Second Language (ESL) learners. We propose Contrastive Learning with Matched Essay Pairs (CL-MEP), a bi-directional alignment strategy. CL-MEP reduces this scoring bias by 39.9% while improving overall accuracy, successfully disentangling valid syntactic complexity from surface-level grammatical errors.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 23 Jan 2026.
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7 of 18 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
- 2020Shortcut Learning in Deep Neural Networkscited by 1,928
- 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
- 2021FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoderscited by 75
- 2024Unveiling the Tapestry of Automated Essay Scoring: A Comprehensive Investigation of Accuracy, Fairness, and Generalizabilitycited by 8
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