@inproceedings{venturini-etal-2026-talktag,
    title = "{T}alk{T}ag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech",
    author = {Venturini, Shamira  and
      Hennh{\"o}fer, Oliver  and
      Kinkel, Steffen  and
      Str{\"o}tgen, Jannik},
    editor = "Liu, Yang Janet  and
      Gessler, Luke",
    booktitle = "Proceedings of the 20th Linguistic Annotation Workshop ({LAW} {XX})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.law-main.20/",
    pages = "309--322",
    ISBN = "979-8-89176-404-0",
    abstract = "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."
}