In an age when demand for innovative and motivating language teaching methodologies is at a very high level, TREAT - the Trilingual REAding Tutor - combines the most advanced natural language processing (NLP) techniques with the latest second and third language acquisition (SLA/TLA) research in an intuitive and user-friendly environment that has been proven to help adult learners (native speakers of L1) acquire reading skills in an unknown L3 which is related to (cognate with) an L2 they know to some extent. This corpus-based methodology relies on existing linguistic resources, as well as materials that are easy to assemble, and can be adapted to support other pairs of L2-L3 related languages, as well. A small evaluation study conducted at the Leeds University Centre for Translation Studies indicates that, when using TREAT, learners feel more motivated to study an unknown L3, acquire significant linguistic knowledge of both the L3 and L2 rapidly, and increase their performance when translating from L3 into L1.

The main goal of the work presented in this paper is to find an inexpensive and automatable way of predicting rankings of MT systems compatible with human evaluations of these systems expressed in the form of Fluency, Adequacy or Informativeness scores. Our approach is to establish whether there is a correlation between rankings derived from such scores and the ones that can be built on the basis of automatically computable attributes of syntactic or semantic nature. We present promising results obtained on the DARPA94 MT evaluation corpus.