An Interdisciplinary Approach to Human-Centered Machine Translation
Marine Carpuat, Omri Asscher, Kalika Bali, Luisa Bentivogli, Frédéric Blain, Lynne Bowker, Monojit Choudhury, Hal Daumé III, Kevin Duh, Ge Gao, Alvin Grissom II, Marzena Karpinska
Abstract
Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between system development and real-world usage, particularly for non-expert users who may struggle to assess translation reliability. This paper advocates for a human-centered approach to MT, emphasizing the alignment of system design with diverse communicative goals and contexts of use. We survey the literature in Translation Studies and Human-Computer Interaction to recontextualize MT evaluation and design to address the diverse real-world scenarios in which MT is used today.
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Cited by top-tier papers2
- Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine TranslationDayeon Ki, Kevin Duh, Marine CarpuatEMNLP 2025
- Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect TranslationsYimin Xiao, Yongle Zhang, Dayeon Ki, Calvin Bao et al.EMNLP 2025
Builds on24
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Robust Speech Recognition via Large-Scale Weak SupervisionAlec Radford, Jong Wook Kim, Tao Xu, Greg Brockman et al.ICML 2023 · 6,966 citations
- How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical MethodologiesOleksandra Vereschak, Gilles Bailly, Baptiste CaramiauxCSCW 2021 · 227 citations
- What is Human-Centered about Human-Centered AI? A Map of the Research LandscapeTara Capel, Margot BreretonCHI 2023 · 218 citations
- Prompting PaLM for Translation: Assessing Strategies and PerformanceDavid Vilar, Markus Freitag, Colin Cherry, Jiaming Luo et al.ACL 2023 · 70 citations
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