  Solving math word problems requires deductive reasoning over the quantities
in the text. Various recent research efforts mostly relied on
sequence-to-sequence or sequence-to-tree models to generate mathematical
expressions without explicitly performing relational reasoning between
quantities in the given context. While empirically effective, such approaches
typically do not provide explanations for the generated expressions. In this
work, we view the task as a complex relation extraction problem, proposing a
novel approach that presents explainable deductive reasoning steps to
iteratively construct target expressions, where each step involves a primitive
operation over two quantities defining their relation. Through extensive
experiments on four benchmark datasets, we show that the proposed model
significantly outperforms existing strong baselines. We further demonstrate
that the deductive procedure not only presents more explainable steps but also
enables us to make more accurate predictions on questions that require more
complex reasoning.
