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For a Special Issue on

Legal Translation and Automation

Abstract deadline
31 May 2024

Manuscript deadline
30 November 2024

Cover image - Perspectives

Special Issue Editor(s)

Gianluca Pontrandolfo, University of Trieste
[email protected]

Carla Quinci, University of Padua
[email protected]

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Legal Translation and Automation

Many myths and deep concerns surround neural machine translation (NMT) and the role specialised translators play in the age of artificial intelligence (AI). The scary idea of ‘human parity’, i.e. the belief that NMT can achieve human quality, sparks off heated debates about the implications of the recent outstanding technological advancement for the translation profession. The alleged threats posed by the results achieved by AI in combination with the gaps in the academic literature about the functioning of (N)MT and its influence on the translation process and product have caused widespread scepticism and mistrust, when not an a priori rejection of NMT. Scholars worldwide have attempted to debunk these myths by studying the actual advantages and disadvantages of using automation. For instance, do Carmo (2023) recently proposed the term “artificial translation” – rather than “machine translation” – to stress that MT does not perform a complete translation process, which would take into account not just the meaning of the source and the target sentences but also extratextual elements e.g. the voice of the author, the intended readers, the purpose of the target texts, which are crucial in any (legal) translation brief (Scott 2019: 81-102).

These concerns are particularly serious in the legal field, where the legal and ethical risks (Canfora & Ottmann 2020, Kenny, Moorkens & do Carmo 2020, Moorkens 2022) related to privacy and confidentiality, together with low risk tolerance and liability, contribute to that feeling of scepticism and mistrust. Thus, legal translation has generally been considered unsuitable for automation (Sánchez-Gijón & Kenny 2022, 85-86), especially due to its inherent challenges. While other specialised fields tend towards conceptual universality and univocity, legal notions and procedures are largely system-bound and historically rooted, which naturally reflects on individual legal languages and culture-bound legal references (cf. Prieto Ramos 2022). This results in incongruities and asymmetries, which represent the typical challenges faced by legal translators (see Biel 2014, 2022, Pontrandolfo 2019, Prieto Ramos 2022). Another “distinctive feature of this specialisation is the high variability of the texts and legal conditions that determine the role of translation itself in each communicative situation, i.e. its communicative priorities between or within legal systems, according to the conventions of specific branches of law and legal genres at the national and international levels” (Prieto Ramos 2022) (see also Cao 2007, Biel 2014, Biel et al. 2019). Legal translation involves negotiating not only between legal languages/discourses but also – and most importantly – between legal systems and legal genres (see Scott 2019: 31-55).

However, the evolution of AI and MT is changing the legal professional landscape, where the ‘triangle of MT’ (quality, price and speed) still plays a pivotal role. Legal translation service providers as well as law firms are increasingly betting on AI and NMT worldwide. Thanks to the growing quality of MT outputs and the development of custom engines (Martínez Domínguez et al. 2020), NMT and machine translation post-editing (MTPE) are now also used in the legal sector. The most recent version of the EMT Competence Framework “acknowledges that [it] represents a growing part of translation workflows, and that MT literacy and awareness of the possibilities and limitations of MT is an integral part of professional translation competence” (EMT Expert Group 2022, 7). Then, the question is not so much if machine translation and post-editing (PE) should be implemented in legal translator training but when, and how they are and will be used by professional translators (Quinci, forthcoming; Quinci & Pontrandolfo 2023).

Against this background, this Special Issue aims at mapping the new opportunities and risks related to fast technological advancement and the rapidly changing landscape of legal translation in training and professional settings by exploring a wide array of issues including, but not limited to, the following:

  • Implications of translation modality (human translation, post-editing, etc.) on the translation process
  • Quality evaluation of AI/MT/MTPE outputs in the legal field
  • Effects of MT, MTPE and/or AI on the translation product from end-users’ perspective
  • MT/AI performance across legal genres and languages
  • Implementation and implications of AI and MT in legal translator training and/or the professional practice
  • Impact of AI/MT/MTPE on legal translators’ creativity
  • Training of MT engines for legal translation purposes
  • Legal Machine Translationese and Post-editese
  • Ethics & legal MT/AI
  • Potential, drawbacks, development, applications and assessment of Large Language Models in legal translation
  • Gender bias in legal MT/AI



Biel, Ł. (2014): Lost in the Eurofog: The Textual Fit of Translated Law, Berlin: Peter Lang

Biel, Ł. (2022): “Translating Legal Texts”. In K. Malmkjær (Ed.), The Cambridge Handbook of Translation. Cambridge: Cambridge University Press, 379-400

Biel, Ł.; J. Engberg; R. Martín Ruano, V. Sosoni (eds.) (2019): Research Methods in Legal Translation and Interpreting: Crossing Methodological Boundaries. London: Routledge

Briva-Iglesias, V. (2021): “Human Translation vs. Machine Translation: A Contrastive Analysis and Factors Involving Machine Translation Use for Legal Translation.” Mutatis Mutandis 14 [2]: 571–600

Cao, D. (2007): Translating Law. Clevedon: Multilingual Matters

Canfora, C., A. Ottman (2020): “Risks in Neural Machine Translation”. Translation Spaces 9 (1): 58–77

do Carmo, F. (2023): “Implications of the Use of Artificial Translation in Legal Settings”. Paper presented to the Words to Deeds conference. Cambridge, 28 January 2023

EMT Expert Group (2022): “EMT Competence Framework 2022” –

Heiss, C., M. Soffritti (2018): “DeepL traduttore e didattica della traduzione dall’italiano in tedesco.” InTRAlinea. Special Issue: Translation and Interpreting for Language Learners (TAIL) – https://

Kenny, D. (2022): Machine Translation for Everyone: Empowering Users in The Age of Artificial Intelligence. (Translation and Multilingual Natural Language Processing 18). Berlin: Language Science Press. DOI: 10.5281/zenodo.6653406

Kenny, D., J. Moorkens, & F. do Carmo (2020): “Fair MT: Towards Ethical, Sustainable Machine Translation”. Translation Spaces 9 (1): 1–11

Killman, J., C. C. Mellinger (eds) (2022): Legal Translation and Interpreting in a Technologized World. Monographic Section of Revista de Llengua i Dret, 74/2022

Killman, J., C. C. Mellinger (2022): “Technologized Legal Translation and Interpreting: Resource Potential, Availability, and Application.” J. Killman; C. C. Mellinger (eds): Legal translation and interpreting in a technologized world. Monographic Section of Revista de Llengua i Dret, 74/2022: 1-8

Martínez Domínguez, R., M. Rikters, A. Vasilevskis, M. Pinnis, P. Reichenberg (2020): “Customized Neural Machine Translation Systems for the Swiss Legal Domain.” In J. Campbell, D. Genzel, B. Huyck, and P. O’Neill-Brown (eds), Proceedings of the 14th Conference of the Association for Machine Translation in the Americas October 6-9, 2020, Volume 2: MT User Track. Association for Machine Translation in the Americas, 217–223

Mileto, F. (2019): “Post-Editing and Legal Translation.” H2D. Revista de Humanidades Digitais 1 [1]

Mitchell, M. (2020): Artificial Intelligence: A Guide for Thinking Humans. London: Penguin

Moorkens, J. (2022): “Ethics and Machine Translation.” In Dorothy Kenny (ed.): Machine Translation For Everyone: Empowering Users in the Age of Artificial Intelligence. Berlin: Language Science Press, 121–140

Pontrandolfo, G. (2019): “Discursive Constraints in Legal Translation: A Genre-Based Analytical Framework.” I. Simonnæs; M. Kristiansen (eds): Legal Translation: Current Issues and Challenges in Research, Methods and Applications. Berlin: Frank & Timme, 155-183

Prieto Ramos, F. (2022): “Legal Translation”. Encyclopedia of Translation and Interpreting (ENTI).

Quinci, C. (forthcoming): “The Impact of Machine Translation on the Development of Info-Mining and Thematic Competences in Legal Translation Trainees: A Focus on Time and External Resources”, The Interpreter and Translator Trainer.

Quinci, C., G. Pontrandolfo (2023): “Testing Neural Machine Translation Against Different Levels of Specialisation. An Exploratory Investigation Across Legal Genres and Languages”, trans-kom, 16(1), 174-209.

Roiss, S. (2021): “Y las máquinas rompieron a traducir… Consideraciones didácticas en relación con la traducción automática de referencias culturales en el ámbito jurídico.” TRANS 25: 491-505

Sánchez-Gijón, P., D. Kenny (2022): “Selecting and Preparing Texts for Machine Translation: Pre-Editing and Writing for a Global Audience.” D. Kenny (ed.), Machine Translation for Everyone: Empowering Users in the Age of Artificial Intelligence. Berlin: Language Science Press, 81–103

Scott, J. R. (2019): Legal Translation Outsourced. Oxford: Oxford University Press.

Stasimioti, M., V. Sosoni (2019): “MT Output and Post-Editing Effort: Insights from a Comparative Analysis of SMT and NMT Output and Implications for Training.” R. Besznyák; M. Fischer; C. Szabó (eds): Teaching Specialised Translation and Interpreting in a Digital Age - Fit-For-Market Technologies, Schemes and Initiatives. Wilmington: Vernon Press, 151-174

Sycz-Opoń, J. (2016): “Machine Translation – Can it Assist in Professional Translation of Contracts?” Comparative Legilinguistics 20: 81-100

Wiesmann, E. (2019): “Machine Translation in the Field of Law: A Study of the Translation of Italian Legal Texts into German.” Comparative Legilinguistics 37: 117–153


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