xlm-r-68018c9a·2 events·first seen Aliases: XLM-R
Researchers introduce VEXMLM, a vocabulary-extended variant of XLM-R targeting Amharic and Tigrinya (Ge'ez-script languages) and evaluated across 19 low-resource African languages. The approach adds 30,000 Ge'ez-script subwords to XLM-R's vocabulary with embedding initialization via subword averaging, followed by two-stage training (continued MLM then task fine-tuning). VEXMLM substantially outperforms XLM-R and Glot500 on QA (87.0 EM vs. 66.0 EM), sentiment analysis, and NER, with OOV-token entity accuracy rising from 81.4% to 94.3% across 11 languages. The work addresses a well-known gap in multilingual NLP: Latin-script-centric tokenizers that fragment non-Latin scripts and inflate OOV rates.
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.