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- W4385990903 abstract "Pre-trained language models have been widely successful, particularly in settings with sufficient training data. However, achieving similar results in low-resource multilingual settings and specialized domains, such as epidemic surveillance, remains challenging. In this paper, we propose hypotheses regarding the factors that could impact the performance of an epidemic event extraction system in a multilingual low-resource scenario: the type of pre-trained language model, the quality of the pre-trained tokenizer, and the characteristics of the entities to be extracted. We perform an exhaustive analysis of these factors and observe a strong correlation between them and the observed model performance on a low-resource multilingual epidemic surveillance task. Consequently, we believe that providing language-specific adaptation and extension of multilingual tokenizers with domain-specific entities is beneficial to multilingual epidemic event extraction in low-resource settings." @default.
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- W4385990903 date "2023-01-01" @default.
- W4385990903 modified "2023-10-14" @default.
- W4385990903 title "Analyzing the Impact of Tokenization on Multilingual Epidemic Surveillance in Low-Resource Languages" @default.
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- W4385990903 doi "https://doi.org/10.1007/978-3-031-41682-8_2" @default.
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