Math Verbaliser

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Template-based Math Verbaliser

We approached the design of this math verbaliser with an NLG pipeline, that uses a template-based realisation method combined with a few word-level rules, to generate textual descriptions. E.g. U-<operand1> simsusa ku-<operand2>.

Templates were designed for addition, subtraction, multiplication, divison, exponents, integration, square roots and equations. Complex sentences can be constructed by nesting the templates into one another.

The math verbaliser is a Python program that runs from the terminal. 

A Stacked Chart Comparing the Proportion of Ratings and the Total Exact Matches (out of 5) of Each Formula; and Displays the Total  Percentage of Each Rating and the Total Exact Matches for the 50 Responses.

68% of all responses were rated as understandable. 
26% were unsure of a description’s understandability.
6% of all responses disagreed on a description’s understandability.

The expressions that received disagreeing or unsure responses, and low exact matches, contained minus, integral or square root operators.

The total number of exact matches across all descriptions is 25 out of 50 (50%).

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