  We present an analysis of the performance of machine learning classifiers on
discriminating between similar languages and language varieties. We carried out
a number of experiments using the results of the two editions of the
Discriminating between Similar Languages (DSL) shared task. We investigate the
progress made between the two tasks, estimate an upper bound on possible
performance using ensemble and oracle combination, and provide learning curves
to help us understand which languages are more challenging. A number of
difficult sentences are identified and investigated further with human
annotation.
