  Math word problems form a natural abstraction to a range of quantitative
reasoning problems, such as understanding financial news, sports results, and
casualties of war. Solving such problems requires the understanding of several
mathematical concepts such as dimensional analysis, subset relationships, etc.
In this paper, we develop declarative rules which govern the translation of
natural language description of these concepts to math expressions. We then
present a framework for incorporating such declarative knowledge into word
problem solving. Our method learns to map arithmetic word problem text to math
expressions, by learning to select the relevant declarative knowledge for each
operation of the solution expression. This provides a way to handle multiple
concepts in the same problem while, at the same time, support interpretability
of the answer expression. Our method models the mapping to declarative
knowledge as a latent variable, thus removing the need for expensive
annotations. Experimental evaluation suggests that our domain knowledge based
solver outperforms all other systems, and that it generalizes better in the
realistic case where the training data it is exposed to is biased in a
different way than the test data.
