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Information × Registration Number 2120U007890, Article popup.category Препринт Title Context-Based Question-Answering System for the Ukrainian Language (AI translated) popup.author Tiutiunnyk SerhiiTiutiunnyk Serhii popup.publication 01-01-2020 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/1898 popup.publisher Description This work presents a context-based question answering model for the Ukrainian language based on Wikipedia articles using Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) model, which takes a context (Wikipedia article) and a question to the context. The result of the model is an answer to the question. The model consists of two parts. The first one is a pre-trained multilingual BERT model, which is trained on the top-100, the most popular languages on Wikipedia articles. The second part is the fine-tuned model, which is trained on the dataset of questions and answers to the Wikipedia articles. The training and validation data is Stanford Question Answering Dataset (SQuAD) (Rajpurkar et al., 2016). There are no question answering datasets for the Ukrainian language. The plan is to build an appropriate dataset with machine translation and use it for the fine-tuning training stage and compare the result with models which were fine-tunedon the other languages. The next experiment is to train a model on the Slavic language datasets before fine-tuning on the Ukrainian language and compare the results. popup.nrat_date 2025-11-05 Close