Is MT Ready for the Next Crisis or Pandemic?

2026-01-15

AI Summary

This study addresses the critical challenge of language barriers that impede effective communication between governments and aid organizations during crises or pandemics, particularly when low-resource languages are involved. It presents the first systematic evaluation of four leading commercial machine translation (MT) systems on the TICO-19 multilingual pandemic corpus, with a focus on high-priority low-resource languages in medical and emergency response contexts. Through end-to-end assessments of translation quality and practical utility, the research reveals significant limitations in current commercial MT systems, demonstrating that they remain insufficiently reliable to support multilingual emergency communication in future global public health emergencies.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Multiagent Systems: Agent Communication
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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. Commercial MT systems are reasonable tools to turn to 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 four commercial MT systems using the TICO-19 dataset, which is composed of pandemic-related sentences from a large set of high priority languages spoken by communities most likely to be affected adversely in the next pandemic. We then assess the current degree of ``readiness''for another pandemic (or epidemic) based on the usability of the output translations.

Problem

• machine translation
• low-resource languages
• crisis communication
• medical domain
• pandemic readiness

Innovation

• machine translation
• low-resource languages
• crisis communication
• TICO-19 dataset
• pandemic readiness