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.
Original language | English |
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Title of host publication | International Conference Recent Advances in Natural Language Processing, RANLP 2021 |
Editors | Galia Angelova, Maria Kunilovskaya, Ruslan Mitkov, Ivelina Nikolova-Koleva |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 830-838 |
Number of pages | 9 |
ISBN (Electronic) | 9789544520724 |
DOIs | |
Publication status | Published - 1 Jan 2021 |
Event | International Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications - Online, Virtual, Unknown Duration: 1 Sept 2021 → 3 Sept 2021 https://ranlp.org/ranlp2021/start.php |
Publication series
Series | International Conference Recent Advances in Natural Language Processing, RANLP |
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ISSN | 1313-8502 |
Conference
Conference | International Conference on Recent Advances in Natural Language Processing |
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Abbreviated title | RANLP 2021 |
Country/Territory | Unknown |
City | Virtual |
Period | 1/09/21 → 3/09/21 |
Internet address |