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

Bai Li, Yi-Te Hsu, Frank Rudzicz


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.
Anthology ID:
N19-1199
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1991–1997
Language:
URL:
https://aclanthology.org/N19-1199
DOI:
10.18653/v1/N19-1199
Bibkey:
Cite (ACL):
Bai Li, Yi-Te Hsu, and Frank Rudzicz. 2019. Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1991–1997, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus (Li et al., NAACL 2019)
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PDF:
https://aclanthology.org/N19-1199.pdf
Video:
 https://aclanthology.org/N19-1199.mp4
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