Computer-assisted L2 learning and translation (CAL2T)

This chapter explores ways in which computer-assisted translation (CAT) into a second language (L2) can serve as an educational bridge for the training of L2 translators and L2 learners. Adapting the notion of CALL (computer-assisted language learning), we introduce the term computer-assisted L2 translation (CAL2T), a new concept which will play a pivotal role in re-conceptualizing L2 translation as a core skill in contemporary translator training, and re-evaluating the pedagogical potential of L2 translation to foster linguistic and intercultural mediation skills. Based on the assumption that L2 learning and translation are overlapping cognitive processes, we first discuss a set of common challenges for both L2 learners and translators. Second, we discuss the use of online corpora and machine translation (MT) as two types of data-driven technology that are useful for L2 learners and translators. Third, we propose a number of L2-related pedagogical activities based on the notions of the Web for Corpus (WfC) and the Web as Corpus (WaC) as well as on latest paradigm in automated translation: neural machine translation (NMT). Finally, we advocate for the adoption of a data-driven learning (DDL) approach to inform hybrid pedagogical activities in educational settings at the crossroads between L2 learning and translation.