Designing a Learner Translator Corpus for Training Purposes

This paper presents the development of the MeLLANGE corpus, a multilingual, aligned and annotated learner translator corpus (LTC). Unlike other learner corpora, MeLLANGE focuses on translation-related rather than language acquisition issues. The corpus contains thus student performance into their mother tongues. Moreover, the intention is not to deliver a repository of student errors, but rather give both trainers and trainees the opportunity to identify possible translation problems, as well as possible solutions, in a multilingual, corpus-based environment. The information attached to the corpus is divided into metadata regarding the source text, the translator and the translation situation, linguistic annotation and error annotation according to an error typology developed in the project. We show examples of how the MeLLANGE corpus can be exploited in translation teaching from a data-driven point of view, and from a data-based point of view.