Using MT for multilingual covid-19 case load prediction from social media texts
Maja Popovic, Vasudevan Nedumpozhimana, Meegan Gower, Sneha Rautmare, Nishtha Jain, John Kelleher
Abstract
In the context of an epidemiological study involving multilingual social media, this paper reports on the ability of machine translation systems to preserve content relevant for a document classification task designed to determine whether the social media text is related to covid. The results indicate that machine translation does provide a feasible basis for scaling epidemiological social media surveillance to multiple languages. Moreover, a qualitative error analysis revealed that the majority of classification errors are not caused by MT errors.- Anthology ID:
- 2023.eamt-1.45
- Volume:
- Proceedings of the 24th Annual Conference of the European Association for Machine Translation
- Month:
- June
- Year:
- 2023
- Address:
- Tampere, Finland
- Editors:
- Mary Nurminen, Judith Brenner, Maarit Koponen, Sirkku Latomaa, Mikhail Mikhailov, Frederike Schierl, Tharindu Ranasinghe, Eva Vanmassenhove, Sergi Alvarez Vidal, Nora Aranberri, Mara Nunziatini, Carla Parra Escartín, Mikel Forcada, Maja Popovic, Carolina Scarton, Helena Moniz
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 461–470
- Language:
- URL:
- https://aclanthology.org/2023.eamt-1.45
- DOI:
- Bibkey:
- Cite (ACL):
- Maja Popovic, Vasudevan Nedumpozhimana, Meegan Gower, Sneha Rautmare, Nishtha Jain, and John Kelleher. 2023. Using MT for multilingual covid-19 case load prediction from social media texts. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pages 461–470, Tampere, Finland. European Association for Machine Translation.
- Cite (Informal):
- Using MT for multilingual covid-19 case load prediction from social media texts (Popovic et al., EAMT 2023)
- Copy Citation:
- PDF:
- https://aclanthology.org/2023.eamt-1.45.pdf
Export citation
@inproceedings{popovic-etal-2023-using,
title = "Using {MT} for multilingual covid-19 case load prediction from social media texts",
author = "Popovic, Maja and
Nedumpozhimana, Vasudevan and
Gower, Meegan and
Rautmare, Sneha and
Jain, Nishtha and
Kelleher, John",
editor = "Nurminen, Mary and
Brenner, Judith and
Koponen, Maarit and
Latomaa, Sirkku and
Mikhailov, Mikhail and
Schierl, Frederike and
Ranasinghe, Tharindu and
Vanmassenhove, Eva and
Vidal, Sergi Alvarez and
Aranberri, Nora and
Nunziatini, Mara and
Escart{\'\i}n, Carla Parra and
Forcada, Mikel and
Popovic, Maja and
Scarton, Carolina and
Moniz, Helena",
booktitle = "Proceedings of the 24th Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2023",
address = "Tampere, Finland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2023.eamt-1.45",
pages = "461--470",
abstract = "In the context of an epidemiological study involving multilingual social media, this paper reports on the ability of machine translation systems to preserve content relevant for a document classification task designed to determine whether the social media text is related to covid. The results indicate that machine translation does provide a feasible basis for scaling epidemiological social media surveillance to multiple languages. Moreover, a qualitative error analysis revealed that the majority of classification errors are not caused by MT errors.",
}
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%0 Conference Proceedings %T Using MT for multilingual covid-19 case load prediction from social media texts %A Popovic, Maja %A Nedumpozhimana, Vasudevan %A Gower, Meegan %A Rautmare, Sneha %A Jain, Nishtha %A Kelleher, John %Y Nurminen, Mary %Y Brenner, Judith %Y Koponen, Maarit %Y Latomaa, Sirkku %Y Mikhailov, Mikhail %Y Schierl, Frederike %Y Ranasinghe, Tharindu %Y Vanmassenhove, Eva %Y Vidal, Sergi Alvarez %Y Aranberri, Nora %Y Nunziatini, Mara %Y Escartín, Carla Parra %Y Forcada, Mikel %Y Popovic, Maja %Y Scarton, Carolina %Y Moniz, Helena %S Proceedings of the 24th Annual Conference of the European Association for Machine Translation %D 2023 %8 June %I European Association for Machine Translation %C Tampere, Finland %F popovic-etal-2023-using %X In the context of an epidemiological study involving multilingual social media, this paper reports on the ability of machine translation systems to preserve content relevant for a document classification task designed to determine whether the social media text is related to covid. The results indicate that machine translation does provide a feasible basis for scaling epidemiological social media surveillance to multiple languages. Moreover, a qualitative error analysis revealed that the majority of classification errors are not caused by MT errors. %U https://aclanthology.org/2023.eamt-1.45 %P 461-470
Markdown (Informal)
[Using MT for multilingual covid-19 case load prediction from social media texts](https://aclanthology.org/2023.eamt-1.45) (Popovic et al., EAMT 2023)
- Using MT for multilingual covid-19 case load prediction from social media texts (Popovic et al., EAMT 2023)
ACL
- Maja Popovic, Vasudevan Nedumpozhimana, Meegan Gower, Sneha Rautmare, Nishtha Jain, and John Kelleher. 2023. Using MT for multilingual covid-19 case load prediction from social media texts. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pages 461–470, Tampere, Finland. European Association for Machine Translation.