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To achieve systematic generalisation, it first makes sense to master simpletasks such as arithmetic. Of the four fundamental arithmetic operations(+,-,$\times$,$\div$), division is considered the most difficult for bothhumans and computers. In this paper we show that robustly learning division ina systematic manner remains a challenge even at the simplest level of dividingtwo numbers. We propose two novel approaches for division which we call theNeural Reciprocal Unit (NRU) and the Neural Multiplicative Reciprocal Unit(NMRU), and present improvements for an existing division module, the RealNeural Power Unit (Real NPU). Experiments in learning division with inputredundancy on 225 different training sets, find that our proposed modificationsto the Real NPU obtains an average success of 85.3$\%$ improving over theoriginal by 15.1$\%$. In light of the suggestion above, our NMRU approach canfurther improve the success to 91.6$\%$.

Learning Division with Neural Arithmetic Logic Modules