@inproceedings{bansal-etal-2026-machine,
title = "Is Machine Translation Ready for the Next Crisis or Pandemic?",
author = "Bansal, Vipasha and
Lewis, William D. and
Mihaylova, Nicole and
Brown, Elizabeth and
Pong, Benjamin",
editor = "Ticona, Belu and
Xia, Fei and
Lewis, William D. and
Anastasopoulos, Antonios",
booktitle = "Proceedings of the First Workshop on Language Technologies for Crisis Preparedness and Response",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.lt4cpr-1.8/",
pages = "78--114",
ISBN = "979-8-89176-450-7",
abstract = "Communication in times of crisis is essential. However, there is often a mismatch between the language of governments, aid providers, doctors, and those to whom they are providing aid. It is reasonable to turn to commercial MT tools in these scenarios. But how effective are these tools for translating to and from low resource languages, particularly in the crisis or medical domain? In this study, we evaluate 5 commercial systems used for MT (Google Translate, Microsoft Translator, GPT, Gemini and Claude) against the TICO-19 dataset, which contains pandemic-related sentences from a large set of high priority languages spoken by communities most likely to be adversely affected in the next pandemic. We assess the current degree of ``readiness'' for this next pandemic based on the usability of the output translations."
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<abstract>Communication in times of crisis is essential. However, there is often a mismatch between the language of governments, aid providers, doctors, and those to whom they are providing aid. It is reasonable to turn to commercial MT tools in these scenarios. But how effective are these tools for translating to and from low resource languages, particularly in the crisis or medical domain? In this study, we evaluate 5 commercial systems used for MT (Google Translate, Microsoft Translator, GPT, Gemini and Claude) against the TICO-19 dataset, which contains pandemic-related sentences from a large set of high priority languages spoken by communities most likely to be adversely affected in the next pandemic. We assess the current degree of “readiness” for this next pandemic based on the usability of the output translations.</abstract>
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%0 Conference Proceedings
%T Is Machine Translation Ready for the Next Crisis or Pandemic?
%A Bansal, Vipasha
%A Lewis, William D.
%A Mihaylova, Nicole
%A Brown, Elizabeth
%A Pong, Benjamin
%Y Ticona, Belu
%Y Xia, Fei
%Y Lewis, William D.
%Y Anastasopoulos, Antonios
%S Proceedings of the First Workshop on Language Technologies for Crisis Preparedness and Response
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-450-7
%F bansal-etal-2026-machine
%X Communication in times of crisis is essential. However, there is often a mismatch between the language of governments, aid providers, doctors, and those to whom they are providing aid. It is reasonable to turn to commercial MT tools in these scenarios. But how effective are these tools for translating to and from low resource languages, particularly in the crisis or medical domain? In this study, we evaluate 5 commercial systems used for MT (Google Translate, Microsoft Translator, GPT, Gemini and Claude) against the TICO-19 dataset, which contains pandemic-related sentences from a large set of high priority languages spoken by communities most likely to be adversely affected in the next pandemic. We assess the current degree of “readiness” for this next pandemic based on the usability of the output translations.
%U https://aclanthology.org/2026.lt4cpr-1.8/
%P 78-114
Markdown (Informal)
[Is Machine Translation Ready for the Next Crisis or Pandemic?](https://aclanthology.org/2026.lt4cpr-1.8/) (Bansal et al., LT4CPR 2026)
ACL
- Vipasha Bansal, William D. Lewis, Nicole Mihaylova, Elizabeth Brown, and Benjamin Pong. 2026. Is Machine Translation Ready for the Next Crisis or Pandemic?. In Proceedings of the First Workshop on Language Technologies for Crisis Preparedness and Response, pages 78–114, Hengqin, China. Association for Computational Linguistics.