As Automated Essay Scoring (AES) systems are increasingly used in high-stakes educational settings, concerns regarding algorithmic bias against English as a Second Language (ESL) learners have increased. Current Transformer-based regression models trained primarily on native-speaker corpora often learn spurious correlations between surface-level L2 linguistic features and essay quality. In this study, we conduct a bias study of a fine-tuned DeBERTa-v3 model using the ASAP 2.0 and ELLIPSE datasets, revealing a constrained score scaling for high-proficiency ESL writing where high-proficiency ESL essays receive scores 10.3% lower than Native speaker essays of identical human-rated quality. To mitigate this, we propose applying contrastive learning with a triplet construction strategy: Contrastive Learning with Matched Essay Pairs. We constructed a dataset of 17,161 matched essay pairs and fine-tuned the model using Triplet Margin Loss to align the latent representations of ESL and Native writing. Our approach reduced the high-proficiency scoring disparity by 39.9% (to a 6.2% gap) while maintaining a Quadratic Weighted Kappa (QWK) of 0.76. Post-hoc linguistic analysis suggests the model successfully disentangled sentence complexity from grammatical error, preventing the penalization of valid L2 syntactic structures.

翻译：随着自动作文评分系统在高风险教育场景中的日益普及，针对英语作为第二语言学习者的算法偏见问题愈发受到关注。当前主要基于母语语料库训练的Transformer回归模型，常会学习到表层二语语言特征与作文质量之间的虚假相关性。本研究使用ASAP 2.0和ELLIPSE数据集对微调后的DeBERTa-v3模型进行偏见分析，发现高水平的ESL写作存在评分压缩现象：在人工评分质量相同的情况下，高水平ESL作文得分比母语者作文低10.3%。为缓解此问题，我们提出采用三重态构建策略的对比学习方法：基于匹配作文对的对比学习。我们构建了包含17,161对匹配作文的数据集，并利用三重边际损失对模型进行微调，以对齐ESL写作与母语写作的潜在表征。该方法将高水平作文的评分差异降低了39.9%（差距缩小至6.2%），同时保持了0.76的二次加权卡帕系数。事后语言分析表明，该模型成功解耦了句子复杂度与语法错误，避免了对有效二语句法结构的惩罚。