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Physical measurements constitute a large portion of numbers in academicpapers, engineering reports, and web tables. Current benchmarks fall short ofproperly evaluating numeracy of pretrained language models on measurements,hindering research on developing new methods and applying them to numericaltasks. To that end, we introduce a novel task, Masked Measurement Prediction(MMP), where a model learns to reconstruct a number together with itsassociated unit given masked text. MMP is useful for both training newnumerically informed models as well as evaluating numeracy of existing systems.In order to address this task, we introduce a new Generative Masked Measurement(GeMM) model that jointly learns to predict numbers along with their units. Weperform fine-grained analyses comparing our model with various ablations andbaselines. We use linear probing of traditional pretrained transformer models(RoBERTa) to show that they significantly underperform jointly trainednumber-unit models, highlighting the difficulty of this new task and thebenefits of our proposed pretraining approach. We hope this frameworkaccelerates the progress towards building more robust numerical reasoningsystems in the future.

Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context