A curated list of works on Human-centered Machine Translation (MT), especially focusing on MT consumed by lay users without professional translators mediation. Works are categorized along factors and use cases dimensions. This list originates from our paper:

Beatrice Savoldi, Alan Ramponi, Matteo Negri, and Luisa Bentivogli. 2025. Translation in the Hands of Many: Centering Lay Users in Machine Translation Interactions. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 13887–13900, Suzhou, China. Association for Computational Linguistics. [cite] [paper]

✨ Contributions. Feel free to suggest additional works by submitting a pull request — instructions here ✨

Please cite our paper [Savoldi et al., EMNLP 2025] if you find this curated list useful in your research:

@inproceedings{savoldi-etal-2025-translation,
    title = "Translation in the Hands of Many: Centering Lay Users in Machine Translation Interactions",
    author = "Savoldi, Beatrice  and
      Ramponi, Alan  and
      Negri, Matteo  and
      Bentivogli, Luisa",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.700/",
    pages = "13887--13900",
    ISBN = "979-8-89176-332-6"
}

• By Factors
  • 👆 Usability
  • ✔️ Trust
  • 📖 Literacy
  • 🔷 Miscellaneous
• By Use Cases
  • 🗣️ Communication
  • 📝 Creative/literary
  • 📕 Education
  • ⚖️ Legal
  • 🏥 Medical/health
  • 🔁 Migration/travelers
  • 🌏 Social/cultural
  • ⚪ Miscellaneous
• Related Surveys/positions

• To Be or Not to Be: A Translation Reception Study of a Literary Text Translated into Dutch and Catalan Using Machine Translation [Guerberof-Arenas and Toral, 2024] 📝
• What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-Centered Study [Savoldi et al., 2024] 🌏
• Ethics and Machine Translation: The End User Perspective [Guerberof-Arenas and Moorkens, 2023] 📝
• Opportunities for Human-Centered Evaluation of Machine Translation Systems [Liebling et al., 2022] ⚪
• Towards Sustainable Use of Machine Translation: Usability and Perceived Quality from the End-User Perspective [Kaspere et al., 2021] ⚪
• Language Translation as a Socio-Technical System: Case-Studies of Mixed-Initiative Interactions [Santy et al., 2021] ⚪
• Measuring the Usability of Machine Translation in the Classroom Context [Yang et al., 2021] 📕
• Unmet Needs and Opportunities for Mobile Translation AI [Liebling et al., 2020] 🔁
• What Is the Impact of Raw MT on Japanese Users of Word: Preliminary Results of a Usability Study Using Eye-Tracking [Guerberof-Arenas et al., 2019] ⚪
• Evaluation and Usability of Back Translation for Intercultural Communication [Shigenobu, 2007] ⚪
• Good Applications for Crummy Machine Translation [Church and Hovy, 1993] ⚪

• Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation [Ki et al., 2025] 🏥
• Google Translate Is Our Best Friend Here: A Vignette-Based Interview Study on Machine Translation Use for Health Communication [Valdez and Guerberof-Arenas, 2025] 🏥 🔁
• Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations [Xiao et al., 2025b] 🌏
• Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences [Briakou et al., 2023] ⚪
• Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors [Mehandru et al., 2023] 🏥
• Migrant Communities Living in the Netherlands and Their Use of MT in Healthcare Settings [Valdez et al., 2023] 🔁 🏥
• Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust [Deng et al., 2022] 🏥
• Opportunities for Human-Centered Evaluation of Machine Translation Systems [Liebling et al., 2022] ⚪
• Understanding and Being Understood: User Strategies for Identifying and Recovering From Mistranslations in Machine Translation-Mediated Chat [Robertson and Díaz, 2022] 🗣️
• Machine translation believability [Martindale et al., 2021] ⚪
• Backtranslation Feedback Improves User Confidence in MT, Not Quality [Zouhar et al., 2021] ⚪
• Effect of Cultural Misunderstanding Warning in MT-Mediated Communication [Pituxcoosuvarn et al., 2020] 🗣️ 🌏
• Evaluating the Effects of Interface Feedback in MT-embedded Interactive Translation [Tsai et al., 2015] ⚪
• Enabling Effective Design of Multimodal Interfaces for Speech-to-Speech Translation System: An Empirical Study of Longitudinal User Behaviors Over Time and User Strategies for Coping with Errors [Shin et al., 2013] 🏥
• Influence of Detecting Inaccurate Messages in Real-Time Remote Text-Based Communication via Machine Translation [Miyabe and Yoshino, 2010] 🗣️
• Improving machine translation by showing two outputs [Xu et al., 2010] 🗣️
• Evaluation and Usability of Back Translation for Intercultural Communication [Shigenobu, 2007] ⚪

• Machine Translation Literacy [Bowker, 2025] 📕
• Google Translate Is Our Best Friend Here: A Vignette-Based Interview Study on Machine Translation Use for Health Communication [Valdez and Guerberof-Arenas, 2025] 🏥 🔁
• Sustaining Human Agency, Attending to Its Cost: An Investigation into Generative AI Design for Non-Native Speakers' Language Use [Xiao et al., 2025a] 🗣️ 🔁
• Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations [Xiao et al., 2025b] 🌏
• Understanding Machine Translation Fit for Language Learning: The Mediating Effect of Machine Translation Literacy [Yang, 2024] 📕
• Using a Game-Based Translation Learning App and Google Apps to Enhance Translation Skills: Amplification and Omission [Chen, 2023] 📕
• DataLitMT – teaching data literacy in the context of machine translation literacy [Hackenbuchner and Krüger, 2023] 📕
• Understanding and Being Understood: User Strategies for Identifying and Recovering From Mistranslations in Machine Translation-Mediated Chat [Robertson and Díaz, 2022] 🗣️
• Three Directions for the Design of Human-Centered Machine Translation [Liebling et al., 2021] ⚪
• Chinese Speakers' Use of Machine Translation as an Aid for Scholarly Writing in English: A Review of the Literature and a Report on a Pilot Workshop on Machine Translation Literacy [Bowker, 2020] 📕
• MTLiteracy—A cognitive view [O’Brien and Ehrensberger-Dow., 2020] 📕 🌏
• Towards a Framework for Machine Translation Literacy [Bowker and Ciro, 2019] 📕
• Enabling Effective Design of Multimodal Interfaces for Speech-to-Speech Translation System: An Empirical Study of Longitudinal User Behaviors Over Time and User Strategies for Coping with Errors [Shin et al., 2013] 🏥
• Influence of Detecting Inaccurate Messages in Real-Time Remote Text-Based Communication via Machine Translation [Miyabe and Yoshino, 2010] 🗣️

• A Research Agenda for Using Machine Translation in Clinical Medicine [Khoong and Rodriguez, 2022] 🏥
• Machine translation in society: Insights from UK users [Vieria et al., 2022] ⚪
• Facilitating Global Team Meetings Between Language-Based Subgroups: When and How Can Machine Translation Help? [Zhang et al., 2022] 🗣️
• A Pragmatic Assessment of Google Translate for Emergency Department Instructions [Taira et al., 2021] 🏥
• Understanding the Societal Impacts of Machine Translation: A Critical Review of the Literature on Medical and Legal Use Cases [Vieira et al., 2021] ⚖️ 🏥
• Leveraging Machine Translation to Support Distributed Teamwork Between Language-Based Subgroups: The Effects of Automated Keyword Tagging [Zhang et al., 2021] 🗣️
• Assessing the Use of Google Translate for Spanish and Chinese Translations of Emergency Department Discharge Instructions [Khoong et al., 2019] 🏥
• Development of Machine Translation Technology for Assisting Health Communication: A Systematic Review [Dew et al., 2018] 🏥
• Gist MT Users: A Snapshot of the Use and Users of One Online MT Tool [Nurminen and Papula, 2018] ⚪
• Two is Better Than One: Improving Multilingual Collaboration by Giving Two Machine Translation Outputs [Gao et al., 2015] 🗣️
• Modeling Workflow to Design Machine Translation Applications for Public Health Practice [Turner et al., 2015] 🏥
• Use of Google Translate in Medical Communication: Evaluation of Accuracy [Patil and Davies, 2014] 🏥
• Round-Trip Translation: What Is It Good For? [Somers, 2005] ⚪
• Does Online Machine Translation Spell the End of Take-Home Translation Assignments? [McCarthy, 2004] 📕
• SYSTRAN on AltaVista: A User Study on Real-Time Machine Translation on the Internet [Yang and Lange, 1998] ⚪

• Sustaining Human Agency, Attending to Its Cost: An Investigation into Generative AI Design for Non-Native Speakers' Language Use [Xiao et al., 2025a] 📖
• Understanding and Being Understood: User Strategies for Identifying and Recovering From Mistranslations in Machine Translation-Mediated Chat [Robertson and Díaz, 2022] ✔️ 📖
• Facilitating Global Team Meetings Between Language-Based Subgroups: When and How Can Machine Translation Help? [Zhang et al., 2022] 🔷
• Leveraging Machine Translation to Support Distributed Teamwork Between Language-Based Subgroups: The Effects of Automated Keyword Tagging [Zhang et al., 2021] 🔷
• Effect of Cultural Misunderstanding Warning in MT-Mediated Communication [Pituxcoosuvarn et al., 2020] ✔️
• Two is Better Than One: Improving Multilingual Collaboration by Giving Two Machine Translation Outputs [Gao et al., 2015] 🔷
• Influence of Detecting Inaccurate Messages in Real-Time Remote Text-Based Communication via Machine Translation [Miyabe and Yoshino, 2010] ✔️ 📖
• Improving machine translation by showing two outputs [Xu et al., 2010] ✔️

• To Be or Not to Be: A Translation Reception Study of a Literary Text Translated into Dutch and Catalan Using Machine Translation [Guerberof-Arenas and Toral, 2024] 👆
• Ethics and Machine Translation: The End User Perspective [Guerberof-Arenas and Moorkens, 2023] 👆

• Machine Translation Literacy [Bowker, 2025] 📖
• Understanding Machine Translation Fit for Language Learning: The Mediating Effect of Machine Translation Literacy [Yang, 2024] 📖
• Using a Game-Based Translation Learning App and Google Apps to Enhance Translation Skills: Amplification and Omission [Chen, 2023] 📖
• DataLitMT – teaching data literacy in the context of machine translation literacy [Hackenbuchner and Krüger, 2023] 📖
• Measuring the Usability of Machine Translation in the Classroom Context [Yang et al., 2021] 👆
• Chinese Speakers' Use of Machine Translation as an Aid for Scholarly Writing in English: A Review of the Literature and a Report on a Pilot Workshop on Machine Translation Literacy [Bowker, 2020] 📖
• MTLiteracy—A cognitive view [O’Brien and Ehrensberger-Dow., 2020] 📖
• Towards a Framework for Machine Translation Literacy [Bowker and Ciro, 2019] 📖
• Does Online Machine Translation Spell the End of Take-Home Translation Assignments? [McCarthy, 2004] 🔷

• Understanding the Societal Impacts of Machine Translation: A Critical Review of the Literature on Medical and Legal Use Cases [Vieira et al., 2021] 🔷

• Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation [Ki et al., 2025] ✔️
• Google Translate Is Our Best Friend Here: A Vignette-Based Interview Study on Machine Translation Use for Health Communication [Valdez and Guerberof-Arenas, 2025] ✔️ 📖
• Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences [Briakou et al., 2023] ✔️
• Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors [Mehandru et al., 2023] ✔️
• Migrant Communities Living in the Netherlands and Their Use of MT in Healthcare Settings [Valdez et al., 2023] ✔️
• Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust [Deng et al., 2022] ✔️
• A Research Agenda for Using Machine Translation in Clinical Medicine [Khoong and Rodriguez, 2022] 🔷
• A Pragmatic Assessment of Google Translate for Emergency Department Instructions [Taira et al., 2021] 🔷
• Understanding the Societal Impacts of Machine Translation: A Critical Review of the Literature on Medical and Legal Use Cases [Vieira et al., 2021] 🔷
• Assessing the Use of Google Translate for Spanish and Chinese Translations of Emergency Department Discharge Instructions [Khoong et al., 2019] 🔷
• Development of Machine Translation Technology for Assisting Health Communication: A Systematic Review [Dew et al., 2018] 🔷
• Modeling Workflow to Design Machine Translation Applications for Public Health Practice [Turner et al., 2015] 🔷
• Use of Google Translate in Medical Communication: Evaluation of Accuracy [Patil and Davies, 2014] 🔷
• Enabling Effective Design of Multimodal Interfaces for Speech-to-Speech Translation System: An Empirical Study of Longitudinal User Behaviors Over Time and User Strategies for Coping with Errors [Shin et al., 2013] ✔️ 📖

• Google Translate Is Our Best Friend Here: A Vignette-Based Interview Study on Machine Translation Use for Health Communication [Valdez and Guerberof-Arenas, 2025] ✔️ 📖
• Sustaining Human Agency, Attending to Its Cost: An Investigation into Generative AI Design for Non-Native Speakers' Language Use [Xiao et al., 2025a] 📖
• Migrant Communities Living in the Netherlands and Their Use of MT in Healthcare Settings [Valdez et al., 2023] ✔️
• Unmet Needs and Opportunities for Mobile Translation AI [Liebling et al., 2020] 👆

• Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations [Xiao et al., 2025b] ✔️ 📖
• What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-Centered Study [Savoldi et al., 2024] 👆
• Effect of Cultural Misunderstanding Warning in MT-Mediated Communication [Pituxcoosuvarn et al., 2020] ✔️
• MTLiteracy—A cognitive view [O’Brien and Ehrensberger-Dow., 2020] 📖

• Opportunities for Human-Centered Evaluation of Machine Translation Systems [Liebling et al., 2022] 👆 ✔️
• Machine translation in society: Insights from UK users [Vieria et al., 2022] 🔷
• Towards Sustainable Use of Machine Translation: Usability and Perceived Quality from the End-User Perspective [Kaspere et al., 2021] 👆
• Three Directions for the Design of Human-Centered Machine Translation [Liebling et al., 2021] 📖
• Machine translation believability [Martindale et al., 2021] ✔️
• Language Translation as a Socio-Technical System: Case-Studies of Mixed-Initiative Interactions [Santy et al., 2021] 👆
• Backtranslation Feedback Improves User Confidence in MT, Not Quality [Zouhar et al., 2021] ✔️
• What Is the Impact of Raw MT on Japanese Users of Word: Preliminary Results of a Usability Study Using Eye-Tracking [Guerberof-Arenas et al., 2019] 👆
• Gist MT Users: A Snapshot of the Use and Users of One Online MT Tool [Nurminen and Papula, 2018] 🔷
• Evaluating the Effects of Interface Feedback in MT-embedded Interactive Translation [Tsai et al., 2015] ✔️
• Evaluation and Usability of Back Translation for Intercultural Communication [Shigenobu, 2007] 👆 ✔️
• Round-Trip Translation: What Is It Good For? [Somers, 2005] 🔷
• SYSTRAN on AltaVista: A User Study on Real-Time Machine Translation on the Internet [Yang and Lange, 1998] 🔷
• Good Applications for Crummy Machine Translation [Church and Hovy, 1993] 👆

• An Interdisciplinary Approach to Human-Centered Machine Translation [Carpuat et al., 2025]
• Natural Language Translation at the Intersection of AI and HCI [Green et al., 2015]
• Translation as Human–Computer Interaction [O'Brien, 2012]