检测英语考试中考生套用模板的作弊行为，提升自动评分公正性。
Automatic Detection of Inauthentic Templated Responses in English Language Assessments
- 基于机器学习识别考生抄袭预设模板的应答
- 模型需定期更新以应对新模板变种
- 适合教育评估系统开发者与考试监管方
在高风险英语语言测试中，低技能考生可能通过使用称为“模板”的记忆内容来应对作文题，从而误导自动化评分系统。本文提出自动化检测不真实、模板化应答（AuDITR）任务，设计基于机器学习的方法解决该问题，并强调在实际应用中定期更新模型的重要性，以持续应对新型模板策略。
原文摘要 · Abstract (English)
In high-stakes English Language Assessments, low-skill test takers may employ memorized materials called ``templates'' on essay questions to ``game'' or fool the automated scoring system. In this study, we introduce the automated detection of inauthentic, templated responses (AuDITR) task, describe a machine learning-based approach to this task and illustrate the importance of regularly updating these models in production.
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