  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.
