- Abstract: We build a machine solver for word problems on the physics of a free falling object under constant acceleration of gravity. Each problem consists of a formulation part, describing the setting, and a question part asking for the value of an unknown. Our solver consists of two long short-term memory recurrent neural networks and a numerical integrator. The first neural network (the labeler) labels each word of the problem, identifying the physical parameters and the question part of the problem. The second neural network (the classifier) identifies what is being asked in the question. Using the information extracted by both networks, the numerical integrator computes the solution. We observe that the classifier is resilient to errors made by the labeler, which does a better job of identifying the physics parameters than the question. Training, validation and test sets of problems are generated from a grammar, with validation and test problems structurally different from the training problems. The overall accuracy of the solver on the test cases is 99.8%.
- TL;DR: We build an automated solver for a class of physics word problems, using a combination of neural networks and a numerical integrator.
- Conflicts: ibm.com, cornell.edu
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