  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 MWP
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 MWPs 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
subsequently 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 experiments, we
demonstrate that without using equations for supervision, our approach achieves
accuracy gains of 4.5% and 32% over the state-of-the-art weakly supervised
approach, on the standard Math23K and AllArith datasets respectively.
Additionally, we curate and release new datasets of roughly 10k MWPs each in
English and in Hindi (a low resource language).These datasets are suitable for
training weakly supervised models. We also present an extension of WARMM to
semi-supervised learning and present further improvements on results, along
with insights.
