Multilingual Epidemiological Text Classification: A Comparative Study
Stephen Mutuvi, Emanuela Boros, Antoine Doucet, Adam Jatowt, Gaël Lejeune, Moses Odeo
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-resourced), 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.- Anthology ID:
- 2020.coling-main.543
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6172–6183
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.543
- DOI:
- 10.18653/v1/2020.coling-main.543
- Cite (ACL):
- Stephen Mutuvi, Emanuela Boros, Antoine Doucet, Adam Jatowt, Gaël Lejeune, and Moses Odeo. 2020. Multilingual Epidemiological Text Classification: A Comparative Study. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6172–6183, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Multilingual Epidemiological Text Classification: A Comparative Study (Mutuvi et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.543.pdf