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Numerical reasoning skills are essential for complex question answering (CQA)over text. It requires opertaions including counting, comparison, addition andsubtraction. A successful approach to CQA on text, Neural Module Networks(NMNs), follows the programmer-interpreter paradigm and leverages specialisedmodules to perform compositional reasoning. However, the NMNs framework doesnot consider the relationship between numbers and entities in both questionsand paragraphs. We propose effective techniques to improve NMNs' numericalreasoning capabilities by making the interpreter question-aware and capturingthe relationship between entities and numbers. On the same subset of the DROPdataset for CQA on text, experimental results show that our additionsoutperform the original NMNs by 3.0 points for the overall F1 score.

Improving Numerical Reasoning Skills in the Modular Approach for Complex Question Answering on Text