Cross-lingual transfer of POS tagger into a low-resource languageStojanoska, Sanja (Avtor) Robnik Šikonja, Marko (Mentor) Ljubešić, Nikola (Komentor) cross-lingual transferpart-of-speech taggingmultilingual language modellow-resource languageMacedonian languageWith the continuous growth of online textual content, machine learning is the only feasible approach for implementing advanced systems for language processing. Although many natural language processing (NLP) applications exist, most of them are anglocentric and low-resourced languages are left behind. We apply a cross-lingual transfer approach from several languages to overcome this limitation. Part-of-speech tagging (POS), a fundamental text processing task, is a prerequisite for a variety of NLP problems. To implement a POS-tagger in the low-resource Macedonian language, we use pretrained multilingual models along with annotated data in Serbian, Croatian and Bulgarian. We show that multilingual models fine-tuned with a set of languages similar to the target language achieve good performance in solving the POS-tagging task.20212021-09-13 13:40:02Magistrsko delo/naloga130311sl