XLM-R

Forgotten Words: Benchmarking NeoBERT for Dementia Detection in Low-Resource Conversational Filipino and English Speech

This paper presents the first NLP-based dementia detection study for Filipino speech, constructing a parallel bilingual dataset of 4,000 DementiaBank-derived transcripts with manual Filipino translations. Five model families are evaluated across monolingual, zero-shot cross-lingual, and bilingual fine-tuning settings. English-trained BERT degrades sharply on Filipino (Macro-F1 = 0.455), but bilingual fine-tuning recovers performance to Macro-F1 = 0.969–0.973 across all transformer models. The key finding is that multilingual clinical NLP performance is driven by linguistic coverage during training rather than model scale or architecture.