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        <title>Measuring User’s Mental Models of Speech Translation in Human-AI Collaboration</title>
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    <abstract>Millions of people use machine translation (MT) tools daily, yet little is known about their perception of what systems can and cannot do. This paper studies users’ mental models of speech translation systems through a new framework based on cross-lingual question answering, where users either accept MT output or request professional re-translation to answer questions based on the information presented in a foreign language. By analyzing user behavior and accuracy trends across varying translation qualities, we examine to what extent they can predict where the system is likely to be wrong, and how this mental model evolves. Users develop stronger mental models with practice, especially when they have some knowledge of the source language, primarily by relying on surface-level error cues. Moreover, providing speech transcriptions can help users develop better mental models. Our results show the promise of cross-lingual question answering as a downstream task for studying MT mental models and advancing our understanding of human–AI collaboration.</abstract>
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%0 Conference Proceedings
%T Measuring User’s Mental Models of Speech Translation in Human-AI Collaboration
%A Han, HyoJung
%A Balepur, Nishant
%A Boyd-Graber, Jordan Lee
%A Carpuat, Marine
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F han-etal-2026-measuring-users
%X Millions of people use machine translation (MT) tools daily, yet little is known about their perception of what systems can and cannot do. This paper studies users’ mental models of speech translation systems through a new framework based on cross-lingual question answering, where users either accept MT output or request professional re-translation to answer questions based on the information presented in a foreign language. By analyzing user behavior and accuracy trends across varying translation qualities, we examine to what extent they can predict where the system is likely to be wrong, and how this mental model evolves. Users develop stronger mental models with practice, especially when they have some knowledge of the source language, primarily by relying on surface-level error cues. Moreover, providing speech transcriptions can help users develop better mental models. Our results show the promise of cross-lingual question answering as a downstream task for studying MT mental models and advancing our understanding of human–AI collaboration.
%U https://aclanthology.org/2026.acl-long.1947/
%P 42051-42065

Markdown (Informal)

[Measuring User’s Mental Models of Speech Translation in Human-AI Collaboration](https://aclanthology.org/2026.acl-long.1947/) (Han et al., ACL 2026)

• Measuring User’s Mental Models of Speech Translation in Human-AI Collaboration (Han et al., ACL 2026)

ACL

• HyoJung Han, Nishant Balepur, Jordan Lee Boyd-Graber, and Marine Carpuat. 2026. Measuring User’s Mental Models of Speech Translation in Human-AI Collaboration. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 42051–42065, San Diego, California, United States. Association for Computational Linguistics.