TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916072365490176 |
|---|---|
| author | Venturini, Shamira Hennhöfer, Oliver Kinkel, Steffen Strötgen, Jannik |
| author_facet | Venturini, Shamira Hennhöfer, Oliver Kinkel, Steffen Strötgen, Jannik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01820 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech Venturini, Shamira Hennhöfer, Oliver Kinkel, Steffen Strötgen, Jannik Computation and Language 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. |
| title | TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2606.01820 |