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) |
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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: | 16 Oct 2020 01:49 |
URI: | https://orca.cardiff.ac.uk/id/eprint/124714 |
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