用对比学习缓解ESL作文评分偏见，让高阶英语学习者不被低估。
Mitigating Bias in Automated Grading Systems for ESL Learners: A Contrastive Learning Approach
- 构建1.7万对匹配作文，用三元组损失对齐非母语与母语写作表征
- 高阶ESL作文评分偏差从10.3%降至6.2%，模型一致性仍达QWK 0.76
- 有效分离句式复杂度与语法错误，避免惩罚合理二语结构
随着自动作文评分（AES）系统在高风险教育场景中日益普及，针对英语作为第二语言（ESL）学习者的算法偏见问题愈发突出。当前基于Transformer的回归模型主要在母语者语料上训练，常将表层二语语言特征与作文质量错误关联。本研究通过对微调后的DeBERTa-v3模型在ASAP 2.0和ELLIPSE数据集上的偏见分析发现：高阶ESL写作在人类评分相同的情况下，得分比母语者低10.3%。为缓解此问题，我们提出基于配对作文的对比学习方法（Contrastive Learning with Matched Essay Pairs），构建包含17,161对匹配作文的数据集，并使用三元组损失函数对齐非母语与母语写作的潜在表征。该方法使高阶评分差距缩小39.9%（降至6.2%），同时保持0.76的二次加权卡帕系数。事后语言学分析表明，模型成功解耦句式复杂度与语法错误，避免对有效的二语句法结构进行惩罚。
原文摘要 · Abstract (English)
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.
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