  Recent success of pre-trained language models (PLMs) has stimulated interest
in their ability to understand and work with numbers. Yet, the numerical
reasoning over measurements has not been formally studied despite their
importance. In this study, we show that PLMs lack the capability required for
reasoning over measurements. Furthermore, we find that a language model trained
on a measurement-rich corpus shows better performance on understanding
measurements. We propose a simple embedding strategy to better distinguish
between numbers and units, which leads to a significant improvement in the
probing tasks.
