  Math Word Problems (MWP) is an important task that requires the ability of
understanding and reasoning over mathematical text. Existing approaches mostly
formalize it as a generation task by adopting Seq2Seq or Seq2Tree models to
encode an input math problem in natural language as a global representation and
generate the output mathematical expression. Such approaches only learn shallow
heuristics and fail to capture fine-grained variations in inputs. In this
paper, we propose to model a math word problem in a fine-to-coarse manner to
capture both the local fine-grained information and the global logical
structure of it. Instead of generating a complete equation sequence or
expression tree from the global features, we iteratively combine low-level
operands to predict a higher-level operator, abstracting the problem and
reasoning about the solving operators from bottom to up. Our model is naturally
more sensitive to local variations and can better generalize to unseen problem
types. Extensive evaluations on Math23k and SVAMP datasets demonstrate the
accuracy and robustness of our method.
