title: Machine Solver for Physics Word Problems
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%.
authors: Megan Leszczynski Jose Moreira
hIndex: Megan Leszczynski 5