MultiTraiNMT at the IATIS 2021

MultiTraiNMT at the IATIS 2021

MultitrainMT will lead a thematic panel in the International Association for Translation and Intercultural Studies Conference to be held in September 2021 at the Universitat Pompeu Fabra (Barcelona):

In particular note panel 11 “(Neural) Machine translation in multilingual ecosystems”. Convenors: Olga Torres Hostench (Universitat Autònoma de Barcelona), Juan Antonio Pérez-Ortiz (Universitat d'Alacant), Caroline Rossi (Université Grenoble-Alpes) & Pilar Sánchez-Gijón (Universitat Autònoma de Barcelona).


Neural machine translation is promoted as a technology that will not only change the ecosystem of professional translation but will also have a profound impact on fields such as education. The technical opacity of contemporary approaches, however, and the legal, environmental and other ethical issues they raise, mean that the wholesale adoption of neural machine translation in various branches of the economy or across society is fraught with difficulty. Against a background of industry hype and often self-serving disruption talk, those involved in both language and translation education are seeking informed, critical and sustainable ways to meet the challenges posed by the increasing use of the technology. Convinced that ecological thinking will be important in such endeavours, the panel organisers wish to invite ecologically-aware contributions whose aim is to exchange knowledge, expertise and resources related to machine translation as an influential factor in the translation ecosystem.

Proposed papers may address, for example:

  • Neural machine translation in multilingual ecosystems.
  • Neural machine translation in the evolving relationship between global, national and minoritized languages in a multilingual world.
  • Ethical, social, political, environmental aspects of neural machine translation: beyond the technical approach.
  • Custom neural machine translation: ecological succession and adaptation in specialised translation ecosystems.
  • Neural machine translation quality: developing new processes and interactions.
  • Making deep learning and neural machine translation accessible for non-technical audiences, in particular for language teachers and learners as multilingual citizens and trainee and professional translators.
  • Teaching materials that address both the technical foundations of machine learning—and especially deep learning—as used in machine translation, as well as the ethical, societal and professional implications of this approach.
  • Sourcing data sets (and protocols for developing data sets) that teachers and learners can use in training their own machine translation systems.
  • Developing engaging activities that allow language learners and translators to co-construct knowledge about neural machine translation.
  • Neural machine translation platforms that non-technical learners can use to gain insight into the internal workings of neural machine translation systems.

Papers are expected to present cutting-edge research, best practices, theory building, or policy development that can inspire dialogue amongst participants.

Panel convenors are members of the Erasmus+ strategic partnership “MultiTraiNMT - Machine Translation training for multilingual citizens”. The aim of MultiTraiNMT is to provide partners with the resources required to adapt to an evolving translation ecosystem. To this end, it will create, evaluate and disseminate open access materials designed to enhance teaching and learning about (neural) machine translation among translation scholars, professional translators, trainee translators, language teachers and language learners.