从黑盒到透明：在大学课堂中利用可解释AI增强自动口译评估
From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms
AI总结：
针对自动口译评估缺乏可解释性等问题，提出集成特征工程、数据增强和可解释机器学习的多维建模框架，利用透明特征和SHAP分析实现强预测性能，为传统人工评估提供透明替代方案并支持学习者自我调节。
摘要：
机器学习的最新进展激发了对自动口译质量评估日益增长的兴趣。然而，现有研究存在对语言使用质量考察不足、由于数据稀缺和不平衡导致建模效果不佳，以及缺乏解释模型预测的努力等问题。为了填补这些空白，我们提出了一个多维建模框架，该框架集成了特征工程、数据增强和可解释机器学习。该方法通过仅使用与构念相关的透明特征并进行Shapley Value (SHAP) 分析，将可解释性置于“黑盒”预测之上。我们的结果在一个新颖的英汉交替传译数据集上展示了强大的预测性能，发现BLEURT和CometKiwi分数是忠实度的最强预测特征，停顿相关特征是流利度的最强预测特征，而中文特定的短语多样性指标是语言使用的最强预测特征。总体而言，通过特别强调可解释性，我们提出了一种可扩展、可靠且透明的替代传统人工评估的方法，促进了为学习者提供详细的诊断反馈，并支持单独的自动分数所无法提供的自我调节学习优势。
英文摘要：
Recent advancements in machine learning have spurred growing interests in automated interpreting quality assessment. Nevertheless, existing research suffers from insufficient examination of language use quality, unsatisfactory modeling effectiveness due to data scarcity and imbalance, and a lack of efforts to explain model predictions. To address these gaps, we propose a multi-dimensional modeling framework that integrates feature engineering, data augmentation, and explainable machine learning. This approach prioritizes explainability over ``black box'' predictions by utilizing only construct-relevant, transparent features and conducting Shapley Value (SHAP) analysis. Our results demonstrate strong predictive performance on a novel English-Chinese consecutive interpreting dataset, identifying BLEURT and CometKiwi scores to be the strongest predictive features for fidelity, pause-related features for fluency, and Chinese-specific phraseological diversity metrics for language use. Overall, by placing particular emphasis on explainability, we present a scalable, reliable, and transparent alternative to traditional human evaluation, facilitating the provision of detailed diagnostic feedback for learners and supporting self-regulated learning advantages not afforded by automated scores in isolation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
