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        <title>Domain Adaptation via Prompt Learning for Alzheimer‘s Detection</title>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Spoken language presents a compelling medium for non-invasive Alzheimer‘s disease (AD) screening, and prior work has examined the use of fine-tuned pretrained language models (PLMs) for this purpose. However, PLMs are often optimized on tasks that are inconsistent with AD classification. Spoken language corpora for AD detection are also small and disparate, making generalizability difficult. This paper investigates the use of domain-adaptive prompt fine-tuning for AD detection, using AD classification loss as the training objective and leveraging spoken language corpora from a variety of language tasks. Extensive experiments using voting-based combinations of different prompting paradigms show an impressive mean detection F1=0.8952 (with std=0.01 and best F1=0.9130) for the highest-performing approach when using BERT as the base PLM.</abstract>
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%0 Conference Proceedings
%T Domain Adaptation via Prompt Learning for Alzheimer‘s Detection
%A Farzana, Shahla
%A Parde, Natalie
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F farzana-parde-2024-domain
%X Spoken language presents a compelling medium for non-invasive Alzheimer‘s disease (AD) screening, and prior work has examined the use of fine-tuned pretrained language models (PLMs) for this purpose. However, PLMs are often optimized on tasks that are inconsistent with AD classification. Spoken language corpora for AD detection are also small and disparate, making generalizability difficult. This paper investigates the use of domain-adaptive prompt fine-tuning for AD detection, using AD classification loss as the training objective and leveraging spoken language corpora from a variety of language tasks. Extensive experiments using voting-based combinations of different prompting paradigms show an impressive mean detection F1=0.8952 (with std=0.01 and best F1=0.9130) for the highest-performing approach when using BERT as the base PLM.
%R 10.18653/v1/2024.findings-emnlp.937
%U https://aclanthology.org/2024.findings-emnlp.937/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.937
%P 15963-15976

Markdown (Informal)

[Domain Adaptation via Prompt Learning for Alzheimer’s Detection](https://aclanthology.org/2024.findings-emnlp.937/) (Farzana & Parde, Findings 2024)

• Domain Adaptation via Prompt Learning for Alzheimer’s Detection (Farzana & Parde, Findings 2024)

ACL

• Shahla Farzana and Natalie Parde. 2024. Domain Adaptation via Prompt Learning for Alzheimer’s Detection. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15963–15976, Miami, Florida, USA. Association for Computational Linguistics.