Lynne Bowker

Transformation and Relocation: The evolving role of the translator in an increasingly technologized profession

Lynne Bowker, School of Translation and Interpretation, University of Ottawa, Canada

Translation tools are becoming increasingly embedded in the translation profession. In particular, machine translation technology continues to advance, and the recent paradigm shift which saw the incorporation of artificial intelligence-based machine learning techniques – an approach referred to as Neural Machine Translation (NMT) – has been accompanied by a growing interest in incorporating this technology into the workflow of professional translators. In this presentation, we will explore the “fit-for-purpose” business model of translation, as well as the emerging concept of “machine translation literacy” – the idea that users of machine translation need to develop a critical approach to this technology in order to decide whether, when, why and how to use it in an informed way. We will consider how this can be integrated into translator training, but also how translators can exercise professional and social responsibility by helping to educate users outside the language professions to approach machine translation with a critical eye.

Evgeny Chukharev-Hudilainen

Learner modeling in computer-assisted language learning: approaches, challenges, applications

Computer-assisted language learning (CALL) tools have the potential of radically increasing the effectiveness of language learning and instruction. Unlike more less technologically-sophisticated approaches to pedagogy, modern CALL systems are designed to be adaptive (i.e. providing personalized learning experiences based on individual student's needs), scalable (i.e. reducing the time teachers need to spend creating instructional materials), and most importantly, effective in helping students attain language-learning goals. The cornerstone of adaptive and scalable CALL is learner modeling, i.e. an approach to representing the learner's current and past states as well as predicting future states with and without a pedagogical intervention. In this presentation, using practical examples of CALL tools that I worked on over the last 7 years, I will give an overview of the current approaches to learner modeling in CALL, and discuss their pedagogical, linguistic, and computational challenges.