Solving Probability Problems in Natural Language

Anton Dries, Angelika Kimmig, Jesse Davis, Vaishak Belle, Luc de Raedt
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
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
more » ... 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).
doi:10.24963/ijcai.2017/556 dblp:conf/ijcai/DriesKDBR17 fatcat:q26wodhlf5b5hghwihvvwrcoma