Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations
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Abstract (arXiv)
As Machine Translation (MT) becomes increasingly commonplace, understanding how the general public perceives and relies on imperfect MT is crucial for contextualizing MT research in real-world applications. We present a human study conducted in a public museum (n=452), investigating how fluency and adequacy errors impact bilingual and non-bilingual users' reliance on MT during casual use. Our findings reveal that non-bilingual users often over-rely on MT due to a lack of evaluation strategies and alternatives, while experiencing the impact of errors can prompt users to reassess future reliance. This highlights the need for MT evaluation and NLP explanation techniques to promote not only MT quality, but also MT literacy among its users.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 58 references that are in the map. First posted on arXiv 11 Oct 2025.
Builds on
20 of 58 references.
References · 20
- 2021Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translationcited by 338
- 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
- 2023xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detectioncited by 265
- 2023GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4cited by 146
- 2023Leveraging GPT-4 for Automatic Translation Post-Editingcited by 56
- 2023Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspectioncited by 44
- 2023LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedbackcited by 39
- 2020The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systemscited by 34
- 2021The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Resultscited by 27
- 2023Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errorscited by 22
- 2018Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MTcited by 21
- 2020Unmet Needs and Opportunities for Mobile Translation AIcited by 14
8 more
- 2023Angler: Helping Machine Translation Practitioners Prioritize Model Improvementscited by 12
- 2025Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answeringcited by 10
- 2025Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactionscited by 8
- 2022Facilitating Global Team Meetings Between Language-Based Subgroups: When and How Can Machine Translation Help?cited by 7
- 2025AskQE: Question Answering as Automatic Evaluation for Machine Translationcited by 7
- 2025Sustaining Human Agency, Attending to Its Cost: An Investigation into Generative AI Design for Non-Native Speakers' Language Usecited by 5
- 2025Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translationcited by 5
- 2024(Dis)placed Contributions: Uncovering Hidden Hurdles to Collaborative Writing Involving Non-Native Speakers, Native Speakers, and AI-Powered Editing Toolscited by 3
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Citing papers per year: 2026: 1.
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