重新评估语法错误分类体系，提升语言学习反馈的准确性。
Revisiting Classification Taxonomy for Grammatical Errors
- 从排他性、覆盖度等四方面系统评估分类体系
- 构建多标签标注的数据集并验证现有体系缺陷
- 适合教育科技与自然语言处理研究者参考
语法错误分类在语言学习系统中至关重要，但现有分类体系常缺乏严格验证，导致不一致和不可靠反馈。本文通过引入系统化定性评估框架，从排他性、覆盖度、平衡性和可用性四个方面重新审视已有分类体系。我们构建了一个高质量的语法错误分类数据集，包含多个分类标签，并基于该框架进行评估。实验揭示了现有分类体系的不足。本研究旨在提升错误分析的精确性与有效性，为语言学习者提供更清晰、可操作的反馈。
原文摘要 · Abstract (English)
Grammatical error classification plays a crucial role in language learning systems, but existing classification taxonomies often lack rigorous validation, leading to inconsistencies and unreliable feedback. In this paper, we revisit previous classification taxonomies for grammatical errors by introducing a systematic and qualitative evaluation framework. Our approach examines four aspects of a taxonomy, i.e., exclusivity, coverage, balance, and usability. Then, we construct a high-quality grammatical error classification dataset annotated with multiple classification taxonomies and evaluate them grounding on our proposed evaluation framework. Our experiments reveal the drawbacks of existing taxonomies. Our contributions aim to improve the precision and effectiveness of error analysis, providing more understandable and actionable feedback for language learners.
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