mlsanityData to 14 Oct 2026

An Interdisciplinary Approach to Human-Centered Machine Translation

arXiv 2506.13468 ↗First posted 16 Jun 2025cs.CL cs.AIcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 56 of its 213 references that are in the map. First posted on arXiv 16 Jun 2025.

Builds on

56 of 213 references.

References · 56

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
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  4. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
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  11. 2021Gender Bias in Machine Translationcited by 117
  12. 2023The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Textcited by 100
38 more
  1. 2023Large language models effectively leverage document-level context for literary translation, but critical errors persistcited by 80
  2. 2018A Survey of Domain Adaptation for Neural Machine Translationcited by 64
  3. 2023Benchmarking Machine Translation with Cultural Awarenesscited by 53
  4. 2016Personalized Machine Translation: Preserving Original Author Traitscited by 47
  5. 2024Breaking the Curse of Multilinguality with Cross-lingual Expert Language Modelscited by 38
  6. 2022Transfer Learning Approaches for Building Cross-Language Dense Retrieval Modelscited by 36
  7. 2020A Set of Recommendations for Assessing Human-Machine Parity in Language Translationcited by 34
  8. 2019Controlling Text Complexity in Neural Machine Translationcited by 32
  9. 2024Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Textscited by 31
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  12. 2021The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Resultscited by 27
  13. 2024How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMscited by 23
  14. 2023Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errorscited by 22
  15. 2024Guiding Large Language Models to Post-Edit Machine Translation with Error Annotationscited by 22
  16. 2025Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMscited by 22
  17. 2018Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MTcited by 21
  18. 2022FRMT: A Benchmark for Few-Shot Region-Aware Machine Translationcited by 18
  19. 2024What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Studycited by 15
  20. 2020Unmet Needs and Opportunities for Mobile Translation AIcited by 14
  21. 2022Controlling Translation Formality Using Pre-trained Multilingual Language Modelscited by 14
  22. 2019DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translationcited by 12
  23. 2022Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Expertscited by 12
  24. 2023Angler: Helping Machine Translation Practitioners Prioritize Model Improvementscited by 12
  25. 2021Backtranslation Feedback Improves User Confidence in MT, Not Qualitycited by 10
  26. 2024SpeechQE: Estimating the Quality of Direct Speech Translationcited by 10
  27. 2025Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answeringcited by 10
  28. 2023Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differencescited by 8
  29. 2025Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactionscited by 8
  30. 2022Facilitating Global Team Meetings Between Language-Based Subgroups: When and How Can Machine Translation Help?cited by 7
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  37. 2025Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translationcited by 5
  38. 2025Automatic Input Rewriting Improves Translation with Large Language Modelscited by 4

50 most cited of 56 All 56 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025Toward Human-Centered AI-Assisted Terminology Workcited by 1
  2. 2026LLM-Powered Automatic Translation and Urgency in Crisis Scenarioscited by 1
  3. 2026Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologismscited by 1
  4. 2026Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intentcited by 1
  5. 2026Ouvia: A User-centered Framework for Measuring Usability of Speech Translation in Real-World Communication Scenarioscited by 0
  6. 2026The Role of Disfluencies in Speech Translationcited by 0