Papers by Nikita Mehandru

2 papers
Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors (2023.emnlp-main)

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Challenge: a major challenge in the practical use of Machine Translation (MT) is that users lack information on translation quality to make informed decisions about how to rely on outputs.
Approach: They evaluate quality estimation feedback in vivo with a human study in a medical setting.
Outcome: The proposed method improves appropriate reliance on MT, but backtranslation helps detect harmful errors.
Viability of Machine Translation for Healthcare in Low-Resourced Languages (2025.emnlp-main)

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Challenge: MT errors are more pronounced in low-resourced languages where human translators are scarce and MT tools perform poorly.
Approach: They propose to use a publicly available machine translation system to analyze machine translation errors in healthcare domains.
Outcome: The proposed system reduces errors in two low-resourced languages for healthcare.

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