mlsanityData to 14 Oct 2026

Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions

arXiv 2502.13780 ↗First posted 19 Feb 2025cs.CL cs.CYcited by 8

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Abstract (arXiv)

Converging societal and technical factors have transformed language technologies into user-facing applications used by the general public across languages. Machine Translation (MT) has become a global tool, with cross-lingual services now also supported by dialogue systems powered by multilingual Large Language Models (LLMs). Widespread accessibility has extended MT's reach to a vast base of lay users, many with little to no expertise in the languages or the technology itself. And yet, the understanding of MT consumed by such a diverse group of users -- their needs, experiences, and interactions with multilingual systems -- remains limited. In our position paper, we first trace the evolution of MT user profiles, focusing on non-experts and how their engagement with technology may shift with the rise of LLMs. Building on an interdisciplinary body of work, we identify three factors -- usability, trust, and literacy -- that are central to shaping user interactions and must be addressed to align MT with user needs. By examining these dimensions, we provide insights to guide the progress of more user-centered MT.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 35 of its 119 references that are in the map. First posted on arXiv 19 Feb 2025.

Builds on

35 of 119 references.

References · 35

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23 more
  1. 2021Putting Humans in the Natural Language Processing Loop: A Surveycited by 40
  2. 2024How Culture Shapes What People Want From AIcited by 23
  3. 2023Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errorscited by 22
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025QE4PE: Word-level Quality Estimation for Human Post-Editingcited by 6
  2. 2025An Interdisciplinary Approach to Human-Centered Machine Translationcited by 6
  3. 2025Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translationcited by 5
  4. 2025Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translationscited by 1
  5. 2025Toward Machine Interpreting: Lessons from Human Interpreting Studiescited by 0
  6. 2026Ouvia: A User-centered Framework for Measuring Usability of Speech Translation in Real-World Communication Scenarioscited by 0
  7. 2026Bridging the Usability Gap: Lessons from Interpreting Studies for Machine Interpreting Designcited by 0
  8. 2026Measuring User's Mental Models of Speech Translation in Human-AI Collaborationcited by 0