RILEC：检测和生成英语学习者文本中的L1俄语干扰错误
RILEC: Detection and Generation of L1 Russian Interference Errors in English Learner Texts
AI总结：
RILEC通过生成语言模型和增强方法，有效检测和生成英语学习者文本中的L1俄语干扰错误。
摘要：
许多学生作文中的错误可以由母语（L1）的影响解释。L1干扰指的是由说话者的母语引起的错误，例如用stadion代替stadium，反映俄语的词汇转写。在本工作中，我们处理了在英语作文中由俄语学习者所写的文本中检测此类错误的任务。我们介绍了RILEC，一个包含超过18000个句子的大规模数据集，结合了来自REALEC的专家标注数据和通过基于规则和神经增强生成的合成示例。我们提出了一种生成受L1启发的错误的框架，使用生成语言模型进行优化，通过PPO、提示基于的控制和基于规则的模式。在RILEC上微调的模型表现出强劲的性能，特别是在词级干扰类型，如转写和时态语义方面。我们发现所提出的增强管道导致了显著的性能提升，使其成为学习者和教师更有效地识别和解决此类错误的潜在有用工具。
英文摘要：
Many errors in student essays can be explained by influence from the native language (L1). L1 interference refers to errors influenced by a speaker's first language, such as using stadion instead of stadium, reflecting lexical transliteration from Russian. In this work, we address the task of detecting such errors in English essays written by Russian-speaking learners. We introduce RILEC, a large-scale dataset of over 18,000 sentences, combining expert-annotated data from REALEC with synthetic examples generated through rule-based and neural augmentation. We propose a framework for generating L1-motivated errors using generative language models optimized with PPO, prompt-based control, and rule-based patterns. Models fine-tuned on RILEC achieve strong performance, particularly on word-level interference types such as transliteration and tense semantics. We find that the proposed augmentation pipeline leads to a significant performance improvement, making it a potentially valuable tool for learners and teachers to more effectively identify and address such errors.
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。
