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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.</abstract>
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%0 Conference Proceedings
%T Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus
%A Li, Bai
%A Hsu, Yi-Te
%A Rudzicz, Frank
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F li-etal-2019-detecting
%X 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.
%R 10.18653/v1/N19-1199
%U https://aclanthology.org/N19-1199
%U https://doi.org/10.18653/v1/N19-1199
%P 1991-1997

Markdown (Informal)

[Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus](https://aclanthology.org/N19-1199) (Li et al., NAACL 2019)

• Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus (Li et al., NAACL 2019)

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.