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
- 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)
- PDF:
- https://preview.aclanthology.org/autopr/N19-1199.pdf
- Data
- OpenSubtitles