  Numerical reasoning skills are essential for complex question answering (CQA)
over text. It requires opertaions including counting, comparison, addition and
subtraction. A successful approach to CQA on text, Neural Module Networks
(NMNs), follows the programmer-interpreter paradigm and leverages specialised
modules to perform compositional reasoning. However, the NMNs framework does
not consider the relationship between numbers and entities in both questions
and paragraphs. We propose effective techniques to improve NMNs' numerical
reasoning capabilities by making the interpreter question-aware and capturing
the relationship between entities and numbers. On the same subset of the DROP
dataset for CQA on text, experimental results show that our additions
outperform the original NMNs by 3.0 points for the overall F1 score.
