Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus

ACL 2019

Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus

Jan 19, 2021
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Abstract: Machine learning has shown promise for automatic detection of Alzheimer's disease (AD) through speech; however, efforts are hampered by a scarcity of data, especially in languages other than English. We propose a method to learn a correspondence between independently engineered lexicosyntactic features in two languages, using a large parallel corpus of out-of-domain movie dialogue data. We apply it to dementia detection in Mandarin Chinese, and demonstrate that our method outperforms both unilingual and machine translation-based baselines. This appears to be the first study that transfers feature domains in detecting cognitive decline. Authors: Bai Li, Yi-Te Hsu, Frank Rudzicz (University of Toronto, Vector Institute, Toronto Rehabilitation Institute, Academia Sinica)

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