  Solving math word problems (MWPs) is an important and challenging problem in
natural language processing. Existing approaches to solve MWPs require full
supervision in the form of intermediate equations. However, labeling every math
word problem with its corresponding equations is a time-consuming and expensive
task. In order to address this challenge of equation annotation, we propose a
weakly supervised model for solving math word problems by requiring only the
final answer as supervision. We approach this problem by first learning to
generate the equation using the problem description and the final answer, which
we then use to train a supervised MWP solver. We propose and compare various
weakly supervised techniques to learn to generate equations directly from the
problem description and answer. Through extensive experiment, we demonstrate
that even without using equations for supervision, our approach achieves an
accuracy of 56.0 on the standard Math23K dataset. We also curate and release a
new dataset for MWPs in English consisting of 10227 instances suitable for
training weakly supervised models.
