There is currently much demand for effective language courses that target specific audiences, as well as specific needs. The current general trend to subordinate teaching best practices to the capabilities of technology is the subject of numerous critical papers, yet little seems to be done in practical terms to explore the alternatives. It is often reported how labour-intensive the creation of a language course is, and it is frequently noticeable that users have only limited access to tailoring a course to their needs - both in terms of being able to choose from enough criteria in order to create their own path and navigate at their own pace through resources, and in terms of being able to expand the resources available to them.\ud \ud This paper demonstrates how comparable corpora, richly annotated by automated NLP techniques, can be successfully exploited for foreign language learning within a web-based environment. Specifically, the reading model developed\ud in this project, together with its practical implementation into a computer-assisted language learning (CALL) environment, are designed to help adult speakers\ud (language LI, here English) acquire reading skills in a foreign language (L3, here Romanian) that is cognate with a second language they know to some extent (L2, here French). The environment - named TREAT (Trilingual REAding Tutor) -\ud dynamically processes user requests to display linguistic information extracted from the corpora that is intended to facilitate reading comprehension. TREAT has also\ud been designed to allow the learners as much freedom as possible, while being always at hand to offer support when needed.\ud \ud A small pilot study was carried out involving Leeds University MA in Applied Translation Studies students, and the results indicate that both my approach and its\ud practical implementation are sound, intuitive and user-friendly. Moreover, I have reasons to believe that this approach also had a positive impact on the learners'\ud command of L2, by exposing them - resources permitting - to authentic input in all of the project languages, activating their passive knowledge of L2 and supporting their hypotheses about and connections between all the project languages.\ud \ud Finally, the reading model developed in this project supports extensions to other pairs of related (L2-L3) languages and the learning environment I have implemented is scalable and easily maintainable. Tools are available to harvest adhoc corpora that reflect the learners' areas of interest.\ud \u
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