Call for abstracts & papers: Human-Centered, Augmented Machine Translation - LANS-TTS issue 25, publication year 2026

07-01-2025

Call for abstracts & papers: Human-Centered, Augmented Machine Translation - LANS-TTS issue 25, publication year 2026

Guest Editors:

  • Vicent Briva-Iglesias (Dublin City University)
  • Sharon O’Brien (Dublin City University)

Overview:
Recent language technology developments have disrupted the translation and interpreting professions. However, the focus has been on using more computational power and training larger language models (do Carmo & Moorkens, 2022), often neglecting the needs of users of such technology (Birhane et al., 2022).

According to Shneiderman (2022), the goal of technology development has been the creation of an intelligent agent that emulates human behaviour to increase automation. As a response, a novel technology design framework has gained a foothold recently: human-centered AI (HCAI), where instead of human emulation, the aim is to produce a powerful tool that augments human capabilities, enhances performance, and empowers users, who are at all instances in supervisory control of such systems (Shneiderman 2022). A key element in the HCAI framework is that of “augmentation”. Human performance is constrained by cognitive load and augmentation seeks to overcome this limitation and to amplify, rather than replace, human intelligence. This shift, moving from emulation to empowerment, places humans at the centre of AI/language technology (Raisamo et al., 2019) instead of just “in the loop”. This reorientation, emphasizing the synergy and collaboration between humans and machines, presages a new era where technology becomes a partner rather than a substitute. In translation and interpreting, this human-centered, augmented approach has been recently suggested (O’Brien, 2023). When applied to machine translation (MT), we can talk about human-centered, augmented MT (HCAMT) (Briva-Iglesias, 2024). Early studies on HCAMT show that, through the analysis of machine translation user experience (MTUX), there are human-MT interactions that augment users, allowing them to be more comfortable with technology, and more in control, while enhancing their performance (Briva-Iglesias et al., 2023).

The successful implementation of HCAMT for translation and interpreting may lead to sustainable, diverse, and ethically sound development and utilisation of MT systems and other technological tools through a wide variety of users and use-cases. Consequently, this special issue calls for proposals that aim to trigger a step change in the point of view from which MT and language technologies are developed and adopted by translators, interpreters and other MT users, and invites proposals that include, but are not limited to:

  • New methodologies for measuring HCAMT experience, fostering tools, workflows and systems in translation, interpreting, and other use-cases.
  • Research on the MTUX of people interacting with MT systems, aiming to identify factors that contribute to effective human-machine collaboration and human empowerment, rather than emulation.
  • Examining how HCAMT can serve a wide variety of users and use-cases, empowering their communication needs, augmenting their abilities, while also promoting diversity and inclusion in language technology applications.
  • Discussion on ethical issues in the development and application of HCAMT, including privacy, bias, inclusivity, and trust.
  • Design and evaluation of systems that facilitate effective collaboration between translators/interpreters and MT technologies.

Submissions are expected to go beyond consideration of user interaction with MT to really engage with the topic of augmentation and empowerment.

Submission Guidelines:
Submissions should adhere to the journal's formatting guidelines. All manuscripts will undergo a rigorous peer-review process to ensure the highest quality and relevance. Please submit abstracts of at least 500 words and no more than 1000 words in English, including relevant references (not included in the word count), to both Vicent Briva-Iglesias (vicent.brivaiglesias@dcu.ie) and Sharon O’Brien (sharon.obrien@dcu.ie) in the same email, before 1 May 2025.

Important Dates:

  • Submission of proposals (abstract) for papers: 1 May 2025 
  • Acceptance of the submitted abstract: 1 July 2025.
  • Submission of papers: 1 December 2025 (around 8,000 words, including references, notes and spaces).
  • Notification of Acceptance: March 2026
  • Submission of the final versions of the papers: 1 June 2026.
  • Publication: December 2026.

Contact Information:
For inquiries about the special issue, please contact the guest editors:

  • Vicent Briva-Iglesias (vicent.brivaiglesias@dcu.ie)
  • Sharon O’Brien (sharon.obrien@dcu.ie)

We look forward to receiving your submissions and advancing the discourse on human-centered, augmented machine translation together.

 

References

Birhane, A., Isaac, W., Prabhakaran, V., Diaz, M., Elish, M. C., Gabriel, I., & Mohamed, S. (2022). Power to the People? Opportunities and Challenges for Participatory AI. Proceedings of the 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, 1–8. https://doi.org/10.1145/3551624.3555290

Briva-Iglesias, V. (2024). Fostering human-centered, augmented machine translation: Analysing interactive post-editing. Dublin City University.

Briva-Iglesias, V., O’Brien, S., & Cowan, B. R. (2023). The impact of traditional and interactive post-editing on Machine Translation User Experience, quality, and productivity: Translation, Cognition & Behavior, 6(1). https://doi.org/10.1075/tcb.00077.bri

do Carmo, F., & Moorkens, J. (2022). Translation’s new high-tech clothes. In The Human Translator in the 2020s (pp. 11–26). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781003223344-2/translation-new-high-tech-clothes-f%C3%A9lix-carmo-joss-moorkens

Nurminen, M., & Papula, N. (2018). Gist MT users: A snapshot of the use and users of one online MT tool.

O’Brien, S. (2023). Human-Centered augmented translation: Against antagonistic dualisms. Perspectives, 1–16. https://doi.org/10.1080/0907676X.2023.2247423

Raisamo, R., Rakkolainen, I., Majaranta, P., Salminen, K., Rantala, J., & Farooq, A. (2019). Human augmentation: Past, present and future. International Journal of Human-Computer Studies, 131, 131–143. https://doi.org/10.1016/j.ijhcs.2019.05.008

Shneiderman, B. (2022). Human-centered AI. Oxford University Press. https://books.google.com/books?hl=en&lr=&id=mSRXEAAAQBAJ&oi=fnd&pg=PP1&dq=info:R2ABLndGsMAJ:scholar.google.com&ots=n1ci4eiM1b&sig=lqnJuid27YA20CFSU8h7Ek5hA7c