mlsanityData to 14 Oct 2026

Is MT Ready for the Next Crisis or Pandemic?

arXiv 2601.10082 ↗First posted 15 Jan 2026cs.CLcited by 2

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 15 Jan 2026.

Builds on

9 of 29 references.

References · 9

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  2. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  3. 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
  4. 2023How Good Are GPT Models at Machine Translation? A Comprehensive Evaluationcited by 348
  5. 2023Hallucinations in Large Multilingual Translation Modelscited by 129
  6. 2014Processing Social Media Messages in Mass Emergency: A Surveycited by 51
  7. 2024LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedbackcited by 51
  8. 2023Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decodingcited by 42
  9. 2024Language model developers should report train-test overlapcited by 16

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Citing papers per year: 2026: 2.

2 papers.