Solving Probability Problems in Natural Language

A. Dries, A. Kimmig, J. Davis, V. Belle, L. De Raedt

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The ability to solve probability word problems such as those found in introductory discrete mathematics textbooks, is an important cognitive and intellectual 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 answers 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 programming 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).
Original languageEnglish
Title of host publicationProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-2017)
PublisherIJCAI Inc
Number of pages7
ISBN (Print)978-0-9992411-0-3
Publication statusPublished - 25 Aug 2017
Event26th International Joint Conference on Artificial Intelligence - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017


Conference26th International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2017
Internet address


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