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Solving probability problems in natural language

Dries, Anton, Kimmig, Angelika, Davis, Jesse, Belle, Vaishak and De Raedt, Luc 2017. Solving probability problems in natural language. Presented at: 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19-25 August 2017. Published in: Sierra, Carles ed. IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI Press, 3981–3987.

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Abstract

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 endto- 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 highlevel 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-toend evaluation, we are able to answer 12.5% of the questions (or 31.1% if we exclude examples not supported by design).

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: AAAI Press
ISBN: 9780999241103
Date of First Compliant Deposit: 5 August 2019
Date of Acceptance: 24 April 2017
Last Modified: 04 Mar 2020 14:30
URI: http://orca-mwe.cf.ac.uk/id/eprint/124714

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