Academic Year 2023/2024

  • Teaching Mode: Traditional lectures
  • Campus: Forli
  • Corso: Second cycle degree programme (LM) in Specialized translation (cod. 9174)

    Also valid for Second cycle degree programme (LM) in Specialized translation (cod. 9174)

Learning outcomes

The student knows the advanced techniques for language service provision in one or more professional settings; s/he is able to analyze and critically assess language services in one or more professional settings, and suggest improvement strategies; s/he is able to acquire further skills related to linguistics, translation and technology, as well as other disciplines of relevance to her/his studies, through interaction with professionals from a range of fields.

Course contents

Translation, inclusion and artificial intelligence

Access to the course is limited (max. 10-15 people)


Excellent knowledge of Italian; knowledge of at least one other language among English, French or Spanish (knowledge of three languages will be particularly appreciated).

Language and computer skills will possibly be assessed by initial selective test.


The course provides a framework on the use of artificial intelligence for the purpose of machine translation and more specifically inclusive translation of administrative texts. The E-MIMIC platform made available by the Politecnico di Torino will be used to build an application for reformulating administrative texts in an inclusive way. You will then have the opportunity to participate in the current project of the same name (https://dbdmg.polito.it/e-mimic/index.php) .

Frontal training will specifically cover the following content:

-choice of corpora for machine translation;
-gender bias, machine translation (intralinguistics) and artificial intelligence;
-inclusive language and reformulation of text from non-inclusive to inclusive;
-use of the E-MIMIC platform and annotation of corpora.

The training also includes practical exercises in:

-retrieval, selection, processing of annotation corpora;
-annotation of corpora and insertion of appropriate reformulation -of text;
-check of annotation work.

Cross-curricular skills will be developed by working in groups, acquiring problem-solving skills, cognitive flexibility and critical thinking, particularly reasoning about deep learning models used by the device, as well as intralinguistic translation and inclusive reformulation techniques in relation to supervised learning of artificial intelligence algorithms.

The course is particularly useful for anyone who wants to acquire interdisciplinary skills (linguistics, translation, computer science) related to new emerging profiles in applied linguistics.


Ad hoc materials will be made available on Virtuale platform.

Below are some general references that will be covered during the course:

Bartoletti I. (2020), An Artificial Revolution: On Power, Politics and AI, Indigo Press.

Consiglio dell’Unione europea (2018). Una comunicazione inclusiva al Segretariato Generale del Consiglio (SGC)


Guidelines E-MIMIC version ITA 1.3 (2022)

Cerquitelli T., Raus R., Cagliero L., Tonti M., Attanasio G., La Quatra M., Greco S. (2022), “L’analyse du discours et l’intelligence artificielle pour réaliser une écriture inclusive : le projet E-MIMIC”, F. Neveu, S. Prévost, A. Steuckardt, G. Bergounioux and B. Hamma (éds), 8ème Congrès mondial de Linguistique française – CMLF 2022,


Cerquitelli T., Raus R., Cagliero L., Tonti M., Attanasio G., La Quatra M., Greco S., E-MIMIC: “Empowering Multilingual Inclusive Communication”, 2021 IEEE International Conference on Big Data (Big Data), IEE, pp. 4227-4234, https://ieeexplore.ieee.org/document/9671868

Mayaffre D., Vanni L. (eds) (2021), L’intelligence artificielle des textes. Des algorithmes à l’interprétation, Champion.

Poibeau T. (2019), Babel 2.0. Où va la traduction automatique, Paris, Odile Jacob.

Vanmassenhove E., Shterionov D., Gwilliam M. (2021), “Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation”, Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, p. 2203–2213


Teaching methods

Weekly lectures of 2 hours each in face-to-face mode starting from the second semester (late February 2024) every Friday for a total of 16 face-to-face hours. The other 60 hours planned will be managed remotely through Teams and will consist of exercises on the E-MIMIC platform provided by the Politecnico di Torino (also with tutor assistance) to be carried out independently or in groups.

It will be possible for those who wish to acquire team leader skills to increase the number of hours of exercises by monitoring group work.

Assessment methods

Assessment of learning will take place directly on the E-MIMIC platform via final exercise to be handed in on time based on the lecturer's instructions.

Teaching tools

Internet, E-MIMIC platform

Office hours

See the website of Rachele Raus


Gender equality

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.