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Using MT for multilingual covid-19 case load prediction from social media texts

Popović, Maja orcid logoORCID: 0000-0001-8234-8745, Vasudevan, Nedumpozhimana orcid logoORCID: 0000-0001-5161-8925, Meegan, Gower orcid logoORCID: 0000-0002-8438-3998, Sneha, Rautmare, Nishtha, Jain and Kelleher, John D. orcid logoORCID: 0000-0001-6462-3248 (2023) Using MT for multilingual covid-19 case load prediction from social media texts. In: 24th Annual Conference of the European Association of Machine Translation 2022 (EAMT 2023), 12-15 Jun 2023, Tampere, Finland. ISBN 978-952-03-2947-1

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-19. 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.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine learning
Computer Science > Machine translating
DCU Faculties and Centres:Research Initiatives and Centres > ADAPT
Published in: 24th Annual Conference of the European Association of Machine Translation 2022 (EAMT 2023), Proceedings. . European Association for Machine Translation (EAMT). ISBN 978-952-03-2947-1
Publisher:European Association for Machine Translation (EAMT)
Official URL:https://events.tuni.fi/uploads/2023/06/a52469c0-pr...
Copyright Information:© 2023 The Authors.
ID Code:28742
Deposited On:12 Jul 2023 09:02 by Maja Popovic . Last Modified 08 Mar 2024 12:22
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