@inproceedings{lindsay-etal-2021-multilingual,
title = "Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task",
author = {Lindsay, Hali and
M{\"u}ller, Philipp and
Kr{\"o}ger, Insa and
Tr{\"o}ger, Johannes and
Linz, Nicklas and
Konig, Alexandra and
Zeghari, Radia and
Verhey, Frans RJ and
Ramakers, Inez HGB},
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.95/",
pages = "830--838",
abstract = "The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines."
}
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<abstract>The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines.</abstract>
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%0 Conference Proceedings
%T Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task
%A Lindsay, Hali
%A Müller, Philipp
%A Kröger, Insa
%A Tröger, Johannes
%A Linz, Nicklas
%A Konig, Alexandra
%A Zeghari, Radia
%A Verhey, Frans RJ
%A Ramakers, Inez HGB
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F lindsay-etal-2021-multilingual
%X The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines.
%U https://aclanthology.org/2021.ranlp-1.95/
%P 830-838
Markdown (Informal)
[Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task](https://aclanthology.org/2021.ranlp-1.95/) (Lindsay et al., RANLP 2021)
ACL
- Hali Lindsay, Philipp Müller, Insa Kröger, Johannes Tröger, Nicklas Linz, Alexandra Konig, Radia Zeghari, Frans RJ Verhey, and Inez HGB Ramakers. 2021. Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 830–838, Held Online. INCOMA Ltd..