Abstract
Beyond enabling linguistic analyses, linguistic annotations may serve as training material for developing automatic language assessment models as well as for providing textual feedback to language learners. Yet these linguistic annotations in their original form are often not easily comprehensible for learners. In this paper, we explore the utilization of GPT-4, as an example of a large language model (LLM), to process linguistic annotations into clear and understandable feedback on their productions for language learners, specifically sign language learners.- Anthology ID:
- 2024.law-1.5
- Volume:
- Proceedings of The 18th Linguistic Annotation Workshop (LAW-XVIII)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julians, Malta
- Editors:
- Sophie Henning, Manfred Stede
- Venues:
- LAW | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 46–60
- Language:
- URL:
- https://aclanthology.org/2024.law-1.5
- DOI:
- Cite (ACL):
- Alessia Battisti and Sarah Ebling. 2024. Automatic Annotation Elaboration as Feedback to Sign Language Learners. In Proceedings of The 18th Linguistic Annotation Workshop (LAW-XVIII), pages 46–60, St. Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Annotation Elaboration as Feedback to Sign Language Learners (Battisti & Ebling, LAW-WS 2024)
- PDF:
- https://preview.aclanthology.org/rocling-reingestion-23/2024.law-1.5.pdf