  Numerical reasoning based machine reading comprehension is a task that
involves reading comprehension along with using arithmetic operations such as
addition, subtraction, sorting, and counting. The DROP benchmark (Dua et al.,
2019) is a recent dataset that has inspired the design of NLP models aimed at
solving this task. The current standings of these models in the DROP
leaderboard, over standard metrics, suggest that the models have achieved
near-human performance. However, does this mean that these models have learned
to reason? In this paper, we present a controlled study on some of the
top-performing model architectures for the task of numerical reasoning. Our
observations suggest that the standard metrics are incapable of measuring
progress towards such tasks.
