Mitigating Bias in Automated Essay Scoring for ESL Learners via Contrastive Learning

Abstract: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.

Computation and Language (cs.CL)

arXiv:2601.16724 [cs.CL]

(or arXiv:2601.16724v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2601.16724 Focus to learn more arXiv-issued DOI via DataCite

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