用大模型自动标注口语中的语法错误，省时省力。
TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech
- 基于大模型微调，自动分析儿童口语中的细粒度语法错误
- 在数据稀缺下仍保持高精度，识别出复杂歧义情况
- 适合语言研究、临床评估等需要大量标注的场景
细粒度形态句法错误标注在临床和发育语言研究中至关重要，但传统方法耗时耗力、依赖专家且难以扩展。本文提出 TalkTag，一个基于大模型的轻量级工具，经过微调后可自动化处理口语转录文本中的 CHAT 风格错误标注。该系统在极低资源条件下（使用儿童叙事数据）训练，验证了在低资源环境下进行语言分析的可行性。评估显示，TalkTag 能生成精准的标注结果，并有效识别出因语言歧义导致的自动化标注难题。总体而言，TalkTag 为手动标注提供了可扩展的替代方案，为形态句法错误标注提供了切实可行的技术支持。
原文摘要 · Abstract (English)
Fine-grained morphosyntactic error annotation is important in clinical and developmental language research, yet it is labour-intensive, expert-dependent, and difficult to scale. We present TalkTag, an LLM-based lightweight tool fine-tuned to automate CHAT-style error annotation in spoken-language transcripts. Developed under conditions of extreme data scarcity using children's narrative data, the system shows the feasibility of linguistic analysis in low-resource settings. Our evaluation demonstrates that TalkTag produces encouragingly precise annotation while effectively identifying instances where linguistic ambiguity makes automated tagging genuinely complex. In summary, with TalkTag, we provide a scalable alternative to manual error annotation and practically viable support for morphosyntactic error annotation.
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