4 June 2018: Vaishak Belle

The ability to solve probability word problems such

as those found in introductory discrete mathematics

textbooks, is an important cognitive and intellec-

tual skill. In this paper, we develop a two-step end-

to-end fully automated approach for solving such

questions that is able to automatically provide an-

swers to exercises about probability formulated in

natural language.

In the first step, a question formulated in natural

language is analysed and transformed into a high-

level model specified in a declarative language.

In the second step, a solution to the high-level

model is computed using a probabilistic program-

ming system.

On a dataset of 2160 probability problems, our

solver is able to correctly answer 97.5% of the

questions given a correct model. On the end-to-

end evaluation, we are able to answer 12.5% of the

questions (or 31.1% if we exclude examples not

supported by design).