  We present ARETA, an automatic error type annotation system for Modern
Standard Arabic. We design ARETA to address Arabic's morphological richness and
orthographic ambiguity. We base our error taxonomy on the Arabic Learner Corpus
(ALC) Error Tagset with some modifications. ARETA achieves a performance of
85.8% (micro average F1 score) on a manually annotated blind test portion of
ALC. We also demonstrate ARETA's usability by applying it to a number of
submissions from the QALB 2014 shared task for Arabic grammatical error
correction. The resulting analyses give helpful insights on the strengths and
weaknesses of different submissions, which is more useful than the opaque M2
scoring metrics used in the shared task. ARETA employs a large Arabic
morphological analyzer, but is completely unsupervised otherwise. We make ARETA
publicly available.
