  Recent work has indicated that many natural language understanding and
reasoning datasets contain statistical cues that may be taken advantaged of by
NLP models whose capability may thus be grossly overestimated. To discover the
potential weakness in the models, some human-designed stress tests have been
proposed but they are expensive to create and do not generalize to arbitrary
models. We propose a light-weight and general statistical profiling framework,
ICQ (I-See-Cue), which automatically identifies possible biases in any
multiple-choice NLU datasets without the need to create any additional test
cases, and further evaluates through blackbox testing the extent to which
models may exploit these biases.

Computation and Language;Artificial Intelligence