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• W3023447687 abstract "As NLP tools become ubiquitous in today's technological landscape, they are increasingly applied to languages with a variety of typological structures. However, NLP research does not focus primarily on typological differences in its analysis of state-of-the-art language models. As a result, NLP tools perform unequally across languages with different syntactic and morphological structures. Through a detailed discussion of word order typology, morphological typology, and comparative linguistics, we identify which variables most affect language modeling efficacy; in addition, we calculate word order and morphological similarity indices to aid our empirical study. We then use this background to support our analysis of an experiment we conduct using multi-class text classification on eight languages and eight models." @default.
• W3023447687 created "2020-05-13" @default.
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• W3023447687 date "2020-04-29" @default.
• W3023447687 modified "2023-09-27" @default.
• W3023447687 title "Evaluating Transformer-Based Multilingual Text Classification" @default.
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• W3023447687 hasPublicationYear "2020" @default.
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