Cross-lingual transfer of resources and models for question answeringDodevska, Lodi (Avtor) Robnik Šikonja, Marko (Mentor) question answeringcross-lingual transferinformation retrievaldeep learningMacedonian languagetransformer modelsImplementing natural language processing (NLP) techniques for low-reso-urce languages is one of the biggest challenges in today's machine learning field. Most state-of-the-art works are focused on well-resourced languages, such as English. However, most languages have scarce resources and it is hard, and in some cases almost impossible, to develop NLP models. We focus on implementation of automatic question answering (QA) models in Macedonian. Since there are no QA datasets in Macedonian yet, we provide the first semi-automatic translation of the SuperGLUE benchmark. Using three question answering datasets from this benchmark (BoolQ, COPA and MultiRC) we fine-tune and compare several transformer-based models. The obtained results show that even in a low-resource language such as Macedonian, we can obtain good results for automatic QA. The translated benchmark and the fine-tuned models can represent a baseline for further research.20222022-10-20 11:25:00Magistrsko delo/naloga142106sl