  Neural models command state-of-the-art performance across NLP tasks,
including ones involving "reasoning". Models claiming to reason about the
evidence presented to them should attend to the correct parts of the input
avoiding spurious patterns therein, be self-consistent in their predictions
across inputs, and be immune to biases derived from their pre-training in a
nuanced, context-sensitive fashion. {\em Do the prevalent *BERT-family of
models do so?} In this paper, we study this question using the problem of
reasoning on tabular data. Tabular inputs are especially well-suited for the
study -- they admit systematic probes targeting the properties listed above.
Our experiments demonstrate that a RoBERTa-based model, representative of the
current state-of-the-art, fails at reasoning on the following counts: it (a)
ignores relevant parts of the evidence, (b) is over-sensitive to annotation
artifacts, and (c) relies on the knowledge encoded in the pre-trained language
model rather than the evidence presented in its tabular inputs. Finally,
through inoculation experiments, we show that fine-tuning the model on
perturbed data does not help it overcome the above challenges.
