Evaluation of MT evaluation measures is limited by inconsistent human judgment data. Nonetheless, machine translation can be evaluated using the well-known measures precision, recall, and their average, the F-measure. The unigram-based F-measure has significantly higher correlation with human judgments than recently proposed alternatives. More importantly, this standard measure has an intuitive graphical interpretation, which can facilitate insight into how MT systems might be improved. The relevant software is publicly available from http://nlp.cs.nyu.edu/GTM/.

This article reviews some recently invented methods for automatically extracting translation lexicons from parallel texts. The accuracy of these methods has been significantly improved by exploiting known properties of parallel texts and of particular language pairs. The state of the art has advanced to the point where non-compositional compounds can be automatically identified with high reliability, and their translations can be found. Most importantly, all of these methods can be smoothly integrated into the usual work ow of MT system developers. Semi-automatic MT lexicon construction is likely to be more efficient and more accurate than either fully automatic or fully manual methods alone.