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Multilingual Epidemiological Text Classification: A Comparative Study

Abstract : In this paper, we approach the multilingual text classification task in the context of the epidemiological field. Multilingual text classification models tend to perform differently across different languages (low-or high-resource), more particularly when the dataset is highly imbalanced, which is the case for epidemiological datasets. We conduct a comparative study of different machine and deep learning text classification models using a dataset comprising news articles related to epidemic outbreaks from six languages, four low-resourced and two high-resourced, in order to analyze the influence of the nature of the language, the structure of the document, and the size of the data. Our findings indicate that the performance of the models based on fine-tuned language models exceeds by more than 50% the chosen baseline models that include a specialized epidemiological news surveillance system and several machine learning models. Also, low-resource languages are highly influenced not only by the typology of the languages on which the models have been pre-trained or/and fine-tuned but also by their size. Furthermore, we discover that the beginning and the end of documents provide the most salient features for this task and, as expected, the performance of the models was proportionate to the training data size.
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https://hal.archives-ouvertes.fr/hal-03089807
Contributor : Gaël Lejeune Connect in order to contact the contributor
Submitted on : Wednesday, January 13, 2021 - 8:48:16 AM
Last modification on : Tuesday, November 16, 2021 - 5:22:00 AM
Long-term archiving on: : Wednesday, April 14, 2021 - 6:03:43 PM

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  • HAL Id : hal-03089807, version 1

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Stephen Mutuvi, Emanuela Boros, Antoine Doucet, Gaël Lejeune, Adam Jatowt, et al.. Multilingual Epidemiological Text Classification: A Comparative Study. COLING, International Conference on Computational Linguistics, Dec 2020, Barcelone, Spain. pp.6172-6183. ⟨hal-03089807⟩

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