Abstract
Machine translation (MT) models have become increasingly accurate and widely accessible for multiple languages in recent years. They can potentially lift the barriers to applying NLP tools and methods to previously unsupported languages and boost comparative cross-lingual research in digital humanities. This study empirically contrasts results obtained with source and target Slovenian ParlaMint corpus of parliamentary debates on topic modelling. It qualitatively compares three steps in topic interpretation: topic description, topic significance in subcorpora, and marginal topic distribution. The results indicate that the topic modelling on the target corpus only partially replicates the topic modelling on the source corpus, but the overlap is sufficient to provide a starting point for the cross-country comparison.
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