We introduce a new method for learning to detect grammatical errors in learner’s writing and provide suggestions. The method involves parsing a reference corpus and inferring grammar patterns in the form of a sequence of content words, function words, and parts-of-speech (e.g., “play ~ role in Ving ” and “look forward to Ving”). At runtime, the given passage submitted by the learner is matched using an extended Levenshtein algorithm against the set of pattern rules in order to detect errors and provide suggestions. We present a prototype implementation of the proposed method, EdIt, that can handle a broad range of errors. Promising results are illustrated with three common types of errors in nonnative writing. 1

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, TransAhead 1, 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). 1.