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

arXiv: https://arxiv.org/abs/1903.00933 · HTML: https://mlsanity.com/paper/1903.00933/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities](https://mlsanity.com/paper/2409.19737/) (2024)
- [MMSE-Calibrated Few-Shot Prompting for Alzheimer's Detection](https://mlsanity.com/paper/2509.19926/) (2025)

## Abstract (arXiv)

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.