  Numbers are essential components of text, like any other word tokens, from
which natural language processing (NLP) models are built and deployed. Though
numbers are typically not accounted for distinctly in most NLP tasks, there is
still an underlying amount of numeracy already exhibited by NLP models. In this
work, we attempt to tap this potential of state-of-the-art NLP models and
transfer their ability to boost performance in related tasks. Our proposed
classification of numbers into entities helps NLP models perform well on
several tasks, including a handcrafted Fill-In-The-Blank (FITB) task and on
question answering using joint embeddings, outperforming the BERT and RoBERTa
baseline classification.
