@inproceedings{li-etal-2019-detecting,
    title = "Detecting dementia in {M}andarin {C}hinese using transfer learning from a parallel corpus",
    author = "Li, Bai  and
      Hsu, Yi-Te  and
      Rudzicz, Frank",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/N19-1199/",
    doi = "10.18653/v1/N19-1199",
    pages = "1991--1997",
    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."
}