Learning to

We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a sys-tem of linear equations, while simultane-ously recovering an alignment of the vari-ables and numbers in these equations to the problem text. The learning algorithm uses varied supervision, including either full equations or just the final answers. We evaluate performance on a newly gathered corpus of algebra word problems, demon-strating that the system can correctly an-swer almost 70 % of the questions in the dataset. This is, to our knowledge, the first learning result for this task.