Nikita Mehandru — accepted AI-conference papers

2025

Viability of Machine Translation for Healthcare in Low-Resourced Languages

Hellina Hailu Nigatu, Nikita Mehandru, Negasi Haile Abadi, Blen Gebremeskel, Ahmed Alaa, Monojit Choudhury

EMNLP 2025

Machine Translation errors in high-stakes settings like healthcare pose unique risks that could lead to clinical harm. The challenges are even more pronounced for low-resourced languages where human translators are scarce and MT tools perform poorly. In this work, we provide a taxonomy of Machine Tr

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2023

Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors

Nikita Mehandru, Sweta Agrawal, Yimin Xiao, Ge Gao, Elaine C Khoong, Marine Carpuat +1

EMNLP 2023long main

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. Progress in quality estimation research provides techniques to automatically assess MT quality, but these techniques have…

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