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    <titleInfo>
        <title>Automatic Annotation Elaboration as Feedback to Sign Language Learners</title>
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            <title>Proceedings of The 18th Linguistic Annotation Workshop (LAW-XVIII)</title>
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            <namePart type="given">Sophie</namePart>
            <namePart type="family">Henning</namePart>
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    <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.</abstract>
    <identifier type="citekey">battisti-ebling-2024-automatic</identifier>
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%0 Conference Proceedings
%T Automatic Annotation Elaboration as Feedback to Sign Language Learners
%A Battisti, Alessia
%A Ebling, Sarah
%Y Henning, Sophie
%Y Stede, Manfred
%S Proceedings of The 18th Linguistic Annotation Workshop (LAW-XVIII)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F battisti-ebling-2024-automatic
%X 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.
%U https://aclanthology.org/2024.law-1.5
%P 46-60

Markdown (Informal)

[Automatic Annotation Elaboration as Feedback to Sign Language Learners](https://aclanthology.org/2024.law-1.5) (Battisti & Ebling, LAW-WS 2024)

• Automatic Annotation Elaboration as Feedback to Sign Language Learners (Battisti & Ebling, LAW-WS 2024)

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.