37.2045, Calls: Mutatis Mutandis, Revista Latinoamericana de Traducción - "Special Issue: New Proposals on Business Translation in the Age of Artificial Intelligence" (Jrnl)

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Subject: 37.2045, Calls: Mutatis Mutandis, Revista Latinoamericana de Traducción - "Special Issue: New Proposals on Business Translation in the Age of Artificial Intelligence" (Jrnl)

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Date: 10-Jun-2026
From: Alba López Díez [alba.lopez at ua.es]
Subject: Mutatis Mutandis, Revista Latinoamericana de Traducción - "Special Issue: New Proposals on Business Translation in the Age of Artificial Intelligence" (Jrnl)

Journal: Mutatis Mutandis, Revista Latinoamericana de Traducción
Issue: New Proposals on Business Translation in the Age of Artificial
Intelligence
Call Deadline: 15-Feb-2027

Machine Translation (MT) and Artificial Intelligence (AI) are now an
integral part of professional practice in business translation.
However, their actual performance in this field remains poorly
studied. The literature on their training application is still scarce,
as reflected in the data from BITRA (Franco Aixelá, 2001-2025), and
most current studies suggest that this type of research is still in an
exploratory phase.
In the professional sphere, references to the performance, quality or
advantages and disadvantages of MT are also limited, perhaps due to
competition or confidentiality within the sector. Nevertheless, it has
been shown that, if implemented strategically, neural MT combined with
translation memories and terminology databases can increase
productivity without compromising quality (Läubli et al., 2019;
Carlucci and Läubli, 2023). Some business experiences confirm that the
success of its integration depends on the appropriate selection of the
system, specific training for post-editors and the adaptation of
workflows (Nunziatini, 2019). In addition, MT systems trained
specifically for the financial field have been shown to achieve
superior results to generalist systems (Luo et al., 2018).
These circumstances have contributed to an ongoing debate, in both
academic and professional circles, about the impact of MT and AI on
business translation. Some authors criticise their effect on job
insecurity, loss of quality, and ethical, or data protection issues,
while others highlight their potential to improve translational
efficiency, and advocate for a redefinition of the translator’s role,
provided that its integration is carried out responsibly and
strategically (González Pastor, 2023).
Some of these insights, also gathered from interviews with industry
professionals (López Díez, 2024), converge on a shared idea: the
translator’s role is shifting from translation itself towards
post-editing and verification, which demands new competences. In this
context, specialisation, critical thinking, and analytical skills
become essential to maintaining the relevance of the human translator,
who may even evolve into a linguistic auditor responsible for ensuring
the quality, coherence, and appropriateness of machine-generated
translations.
Regarding the quality of MT in the business field, one of the
prevailing ideas among trainer-researchers is that general systems may
lead to a false perception of accuracy, particularly among trainee
translators who place excessive trust in the apparent fluency and
grammatical correctness of machine-generated texts, neglecting
conceptual and terminological precision (Martínez Blasco, 2022). And
although neural MT post-editing may improve the performance of
students with lower translation skills, as well as reduce the quality
of translations by more advanced ones (Schumacher, 2023), both groups
remain prone to conceptual misinterpretations.
While several taxonomies of common errors exist (many of which are not
specific to the business field), there are still few studies that
systematically analyse the terminological and phraseological errors
specific to business translation (Peraldi, 2016; Mantrana Gallego,
2024). In this regard, the widely held claim that MT leads to
terminological errors, although supported by some case studies
(Kriston, 2017; Song & Hu, 2021; Valero Cuadra, 2023), lacks a solid
empirical basis in this area of specialisation. It would therefore be
necessary to determine, among other issues, which subfields generate
the most domain-specific errors, how these affect the comprehension
and reliability of the translated message, and to what extent human
post-editing, still under-researched not only in economics but in
general (Koponen et al., 2021), actually corrects such errors or,
conversely, introduces new terminological or phraseological
inconsistencies.
In the educational sphere, there is still a tendency to use MT and AI
in the classroom to highlight their limitations and encourage critical
thinking (Alcalde Peñalver, 2016; Douar, 2022). Yet it remains
questionable whether this approach is truly effective for business
translation, which by definition requires in-depth specialised
knowledge to interpret any text accurately, whether original or
machine-translated. Recent training programmes for financial
translators, such as those offered by the Université d’été de la
traduction financière or Finanzas en Español, argue that a deep
understanding of content remains the cornerstone of learning. In fact,
the founding motto of the Université d’été, “Traduire, c’est avant
tout comprendre,” has remained unchanged since the 1980s.
Therefore, teaching business translation with MT or AI tools must be
grounded in the acquisition of specialised knowledge, as proposed by
authors such as Maldussi (2023, 2024). Such knowledge enables
translators and post-editors to interpret the semantic, collocational,
and logical relationships within business texts correctly. Only
through such comprehensive understanding is it possible to develop
competent post-editing skills capable of offsetting MT’s limitations
regarding conceptual coherence, lexical cohesion, and terminological
precision (Liu, 2020; Tang & Moindjie, 2025).
In line with this reasoning, this special issue invites submissions of
research articles that integrate interdisciplinary perspectives on the
following topics, as well as other related areas offering new insights
into the intersection between translation, economics, and technology:
- Systematic evaluations of terminological and phraseological errors
generated by MT systems in specific subfields of economics.
- Development and evaluation of MT systems tailored to the business
domain, with special emphasis on dataset creation, training
methodologies, empirical comparison with general systems, and result
replicability.
- Design and validation of post-editing guidelines for business
documents based on criteria of quality, terminological coherence, and
efficiency.
- Experimental research on the impact of specialised economic
knowledge on post-editing quality, textual coherence, and
terminological accuracy.
- Educational experiences that effectively integrate MT, AI, or
post-editing into the training and learning of business translation as
tools for accessing specialised knowledge.
- Studies on prompt engineering applied to business translation, with
a focus on controlled generation and terminological adaptation.
- Social research using surveys, interviews, or other empirical
methods to shed light on the use, perception, and acceptance of MT and
AI among professional translators, trainers, and students of business
translation.
References:
Alcalde Peñalver, E. (2016). La traducción automática y el lenguaje
controlado en el ámbito de la traducción financiera. En E. Martínez
Rodrigo, P. Raya-González, & L. Martínez Rolán (coords.),
Investigación, desarrollo e innovación universitarios (pp. 1-9).
McGraw-Hill.
Carlucci, M., & Läubli, S. (2023). Is Generic Machine Translation
Useful for Translating Domain-specific Texts? A Productivity Study in
the Ophthalmology Domain. En S. Castilho, R. Caro Quintana, M.
Stasimioti, & V. Sosoni (eds.), Proceedings of the New Trends in
Translation and Technology Conference - NeTTT 2022 (pp. 75-81).
INCOMA. http://acl-anthology.online/nettt-2022/
Douar, A. (2022). Issues and Perspectives on the Electronic
Translation of Shipping Incoterms: FOB as a case study. Traduction et
Langues, 21(2), 230-247. https://doi.org/10.52919/translang.v21i2.916
Franco Aixelá, J. (2001-2025). BITRA (Bibliografía de Interpretación y
Traducción). Base de datos en acceso abierto.
https://doi.org/10.14198/bitra
González Pastor, D. (coord.) (2023). El impacto de la traducción
automática y posedición en el sector de la traducción en España.
Informe de investigación DITAPE 2022.
https://roderic.uv.es/handle/10550/85779
Koponen, M., Nunes Vieira, L., & Spinolo, N. (2021). Introducció al
Dossier “Estudi de la interacció persona-ordinador en traducció i
interpretació: programari i aplicacions. Tradumàtica Tecnologies de la
Traducció, 19, 66-74. https://doi.org/10.5565/rev/tradumatica.295
Kriston, A. (2017). Machine translation in translating business texts:
Myth versus reality. Professional Communication and Translation
Studies, 10, 126-136. https://doi.org/10.59168/EZWB7156
Läubli, S., Amrhein, C., Düggelin, P., Gonzalez, B., Zwahlen, A., &
Volk, N. (2019). Post-editing Productivity with Neural Machine
Translation: An Empirical Assessment of Speed and Quality in the
Banking and Finance Domain. En M. Forcada, A. Way, B. Haddow, & R.
Sennrich (eds.), Proceedings of Machine Translation Summit XVII:
Research Track (pp. 267-272). European Association for Machine
Translation. https://aclanthology.org/W19-6626/
Liu, L. (2020). 论英汉机器翻译的改进之道——基于《金融时报》人机翻译的比较研究 [Way to Better
English-Chinese Machine Translation Based on Contrastive Study of
Machine and Human Translation of The Financial Times]. 宜春学院学报 [Journal
of Yichun University], 1, 74-78.
López Díez, A. (2024). Perspectivas de traductores económicos
profesionales sobre la investigación académica. Estudio cualitativo
basado en entrevistas. ReCIT: Revista del Área de Traductología, 8,
1-15. https://revistas.unc.edu.ar/index.php/ReCIT/article/view/45410
Luo, L., Yang, H., Siu, S.C., & Chin, F.Y.L. (2018). Neural Machine
Translation for Financial Listing Documents. En L. Cheng, A. Leung, &
S. Ozawa (eds.), Neural Information Processing. ICONIP 2018. Lecture
Notes in Computer Science, vol 11305 (pp. 232-243). Springer.
https://doi.org/10.1007/978-3-030-04221-9_21
Maldussi, D. (2023). La didactique de la traduction économique et
financière à l’épreuve de la sous-compétence thématique : à la
recherche d’un deuxième souffle. Meta, 68(3), 537-555.
https://doi.org/10.7202/1111956ar
Maldussi, D. (2024). Propédeutique de la traduction financière.
Discontinuité et contre-intuitivité. Aracne.
Mantrana Gallego, D. (2024). Evaluation of economic-financial DE-ES
translations: A comparative study between DeepL and Google Translate.
En D. Gallego Hernández (ed.), Translation and Teaching in the
Economic Field: Challenges in the Age of Machine Translation? (pp.
87-104). Peter Lang. https://doi.org/10.3726/b21499
Martínez Blasco, I. (2022) La evaluación y posedición de textos
económicos y financieros pertenecientes al ámbito de los organismos
económicos internacionales con fines formativos. En A. Gómez
González-Jover, & R. Martínez Motos (eds.), Traducción e
interpretación en entornos institucionales. Enseñanza y práctica de la
profesión desde perspectivas sociales e innovadoras (pp. 267-286).
Peter Lang. https://doi.org/10.3726/b17865
Nunziatini, M. (2019). Machine Translation in the Financial Services
Industry: A Case Study. En M. Forcada, A. Way, J. Tinsley, D.
Shterionov, C. Rico, & F. Gaspari (eds.), Proceedings of Machine
Translation Summit XVII: Translator, Project and User Tracks (pp.
57-63). European Association for Machine Translation.
https://aclanthology.org/W19-6709/
Peraldi, S. (2016). De la traduction automatique brute à la
post-édition professionnelle évoluée: le cas de la traduction
financière. Revue française de linguistique appliquée, XXI(1), 67-90.
https://doi.org/10.3917/rfla.211.0067
Schumacher, P. (2023). Traduction humaine et post-édition: contrôle
qualité en contexte académique. Meta, 68(3), 510-536.
https://doi.org/10.7202/1111955ar
Song, J., & Hu, F. (2021). “机器翻译+译后编辑”模式下的经济史文本翻译研究
——以《20世纪经济理论知识史(1890—1918): 资本主义黄金时代的经济学》自译章节为例 [Research on the
Application of Translation Mode of Machine Translation Plus
Post-Editing to the Translation of Economic Texts]. 河南工业大学学报 (社会科学版)
[Journal of Henan University of Technology (Social Science)], 37(4),
13-20. http://dianda.cqvip.com/Qikan/Article/Detail?id=7105729702
Tang, N., & Moindjie, M. (2025). Lexical Cohesion in English-Chinese
Business Translation: Human Translators Versus ChatGPT. World Journal
of English Language, 15(2), 286-295.
https://doi.org/10.5430/wjel.v15n2p286
Valero Cuadra, P. (2023). La traducción automática y la posedición en
el aula de traducción alemán-español: el caso de los textos
económico-financieros. في الترجمة [In translation], 10(1), 41-61.
https://www.asjp.cerist.dz/index.php/en/article/216862
Specifications for Submitting Proposals:
Proposals must:
- Be written in English, French, or Spanish.
- Include the title, authors’ names, institution, and email address.
- Be between 700 and 1000 words in length, including references.
- Present the following structure: introduction, objectives,
methodology, results, implications, and references.
- Be clear, precise, coherent, and concise.
Submissions:
Proposals should be sent to the journal’s email address
(rvmutatismutandis.id at udea.edu.co) with the subject line: Proposal for
the special issue ‘New proposals on business translation in the age of
artificial intelligence’.
Format:
Final articles must be written in English, French or Spanish and be
formatted in accordance with the guidelines on the journal’s website:
https://revistas.udea.edu.co/index.php/mutatismutandis/about/submissions
Timeline:
Submission of proposals: until 15 February 2027
Notification of acceptance or rejection of the proposal: 30 March 2027
Submission of the finished article through Mutatis Mutandis journal
system: until 31 August 2027
Peer review process: 1 September to 31 October 2027
Review and editing: 1 to 30 November 2027
Layout and proofreading: 1 to 31 December 2027
Publication: January 2028
More information in:
https://revistas.udea.edu.co/index.php/mutatismutandis/announcement/view/1311

Linguistic Field(s): Translation

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