标题： 减轻自动口语评估中的数据不平衡 显示英文标题

标题： Mitigating Data Imbalance in Automated Speaking Assessment

摘要： 自动口语评估（ASA）在评估第二语言（L2）学习者的语言能力方面起着至关重要的作用。 然而，ASA模型经常受到类别不平衡的影响，导致预测存在偏差。 为了解决这个问题，我们引入了一种新的训练ASA模型的目标函数，称为平衡logit变化（BLV）损失，它通过扰动模型预测来改善少数类的特征表示，而无需修改数据集。 在ICNALE基准数据集上的评估表明，将BLV损失整合到一个著名的基于文本（BERT）模型中，显著提高了分类准确性和公平性，使自动口语评估对多样化的学习者更加稳健。 显示英文摘要

摘要： Automated Speaking Assessment (ASA) plays a crucial role in evaluating second-language (L2) learners proficiency. However, ASA models often suffer from class imbalance, leading to biased predictions. To address this, we introduce a novel objective for training ASA models, dubbed the Balancing Logit Variation (BLV) loss, which perturbs model predictions to improve feature representation for minority classes without modifying the dataset. Evaluations on the ICNALE benchmark dataset show that integrating the BLV loss into a celebrated text-based (BERT) model significantly enhances classification accuracy and fairness, making automated speech evaluation more robust for diverse learners.