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Automatic Annotation Elaboration as Feedback to Sign Language Learners

Battisti, Alessia; Ebling, Sarah (2024). Automatic Annotation Elaboration as Feedback to Sign Language Learners. In: The 18th Linguistic Annotation Workshop (LAW-XVIII), St. Julian's, Malta, 22 March 2024.

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.

Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
08 Research Priority Programs > Digital Society Initiative
06 Faculty of Arts > Zurich Center for Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Scopus Subject Areas:Physical Sciences > Computational Theory and Mathematics
Physical Sciences > Software
Social Sciences & Humanities > Linguistics and Language
Language:English
Event End Date:22 March 2024
Deposited On:09 May 2024 10:24
Last Modified:29 Mar 2025 04:36
OA Status:Green
Official URL:https://aclanthology.org/2024.law-1.5/
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