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Cross Lingual News Article Classification and Automatic Topic Discovery Using Multilingual Language Models
Dufková, Aneta ; Fajčík, Martin (oponent) ; Kesiraju, Santosh (vedoucí práce)
The goal of this thesis is to perform cross-lingual classification and automatic topic discovery of news articles using pre-trained multilingual language models. For this task, no large multilingual dataset is available, so the first contribution of this thesis is to create one. The other aim of this thesis is to benchmark multilingual embedding models LaBSE and LASER2 in a classification task. This is done through various experiments, such as training on a limited number of articles and naturally zero-shot learning. Then, a topic discovery is performed so that an article can be represented not only by categories but also by the most representative words. Lastly, the results of classification and topic discovery are visualized in a simple web application.

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