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Crosslingual Transfer in Question Answering
 Macková, Kateřina ; Straka, Milan (advisor) ; Rosa, Rudolf (referee)
Question answering is a computer science discipline in the field of natural language processing and information retrieval. The goal is to build a system that can automatically find an answer to a certain question in the text. Nowadays, there exist a lot of models trained on huge training data sets in English. This work focuses on building similar models in Czech without having any Czech training datasets. In this work, we have used SQuAD 1.1 and translated it to Czech to create training and development datasets. Then, we have trained and tested BiDirectional Attention Flow and BERT models. The best obtained result on the Czech dataset is from BERT model trained on Czech with exact match 60.48% and F1 score 73.46%. In addition, we have also trained BERT model on English dataset and we have evaluated it on Czech testing dataset without translation. We have reached exact match 63.71% and F1 score 74.78%, which is extremely good in spite of the fact that the model has not seen any Czech question answering data before. Such a model is very flexible and provide a question answering system in any language for which we have enough monolingual raw texts.
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