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This paper establishes the first benchmark for Tajik POS tagging by comparing BiLSTM-CRF, XLM-RoBERTa, mBERT, ParsBERT, and ruBERT on the TajPersParallel corpus using context-independent lexical units. The study found that mBERT fine-tuned with LoRA achieved the best performance (macro F1 = 0.11, weighted F1 = 0.62), but all models struggled with morphological ambiguity due to the lack of syntactic context. Zero-shot evaluation indicated typological proximity between Tajik, Persian, and Russian.
Even state-of-the-art multilingual models struggle to tag parts-of-speech in Tajik when trained on isolated words, highlighting the critical role of syntactic context.
This paper presents the first benchmark for the task of automatic part-of-speech (POS) tagging for the Tajik language. Despite the existence of multilingual language models demonstrating high effectiveness for many of the world's languages, their capacity for grammatical analysis of Tajik has remained unexplored until now. The aim of this study is to fill this gap through a systematic comparison of classical neural network architectures and modern multilingual transformers. Experiments were conducted on the TajPersParallel corpus, a parallel lexical resource comprising approximately 44,000 dictionary entries. Due to the absence of full-fledged example sentences in the current version of the corpus, the task was performed at the level of isolated lexical units, representing a challenging case of context-independent classification. The study compares the following architectures: a recurrent BiLSTM-CRF model, as well as multilingual models XLM-RoBERTa (large), mBERT, ParsBERT (Persian), and ruBERT (Russian), adapted using the parameter-efficient fine-tuning method LoRA. The testing results showed that the best performance is achieved by the mBERT + LoRA model (macro F1-score = 0.11, weighted F1-score = 0.62). It was established that in the absence of syntactic context, all models experience significant difficulty in resolving morphological ambiguity, successfully classifying primarily high-frequency classes ("noun,""adjective") while demonstrating zero effectiveness for rare function words. Zero-shot evaluation revealed the greatest typological proximity of Tajik to Persian (ParsBERT) and Russian (ruBERT). The obtained results form a foundation for further research and development in the field of automatic processing of the Tajik language.
