Abstract
We introduce a method for learning to predict the following grammar and text of the ongoing translation given a source text. In our approach, predictions are offered aimed at reducing users' burden on lexical and grammar choices, and improving productivity. The method involves learning syntactic phraseology and translation equivalents. At run-Time, the source and its translation prefix are sliced into ngrams to generate subsequent grammar and translation predictions. We present a prototype writing assistant, TransAhead1, that applies the method to where computer-Assisted translation and language learning meet. The preliminary results show that the method has great potentials in CAT and CALL (significant boost in translation quality is observed).