mBERT with LoRA achieves the best weighted F1 of 0.62 for Tajik POS tagging on context-free dictionary entries, but macro F1 is only 0.11, with all models failing on rare function words.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Benchmarking POS Tagging for the Tajik Language: A Comparative Study of Neural Architectures on the TajPersParallel Corpus
mBERT with LoRA achieves the best weighted F1 of 0.62 for Tajik POS tagging on context-free dictionary entries, but macro F1 is only 0.11, with all models failing on rare function words.