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Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers

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Academic year: 2020

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Figure

Figure 1: Modelling numerals with a categoricaldistribution over a fixed vocabulary maps all out-of-vocabulary numerals to the same type, e.g
Figure 2:Mixture of Gaussians model.Theprobability of a numeral is decomposed into theprobability of its decimal precision and the probabilitythat an underlying number will produce the numeralwhen rounded at the given precision.
Table 1: Statistical description of the clinical and sci-entific datasets: Number of instances (i.e
Table 2: Test set perplexities for the clinical and scientific data. Adjusted perplexities (APP) are directlycomparable across all data and models, but perplexities (PP) are sensitive to the varying out-of-vocabulary rates.
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