A Primer for Neural Arithmetic Logic Modules

Neural Arithmetic Logic Modules have become a growing area of interest, though remain a niche field. These units are small neural networks which aim to achieve systematic generalisation in learning arithmetic operations such as {+, -, *, \} while also being interpretive in their weights. This paper is the first in discussing the current state of progress of this field, explaining key works, starting with the Neural Arithmetic Logic Unit (NALU). Focusing on the shortcomings of NALU, we provide an in-depth analysis to reason about design choices of recent units. A cross-comparison between units is made on experiment setups and findings, where we highlight inconsistencies in a fundamental experiment causing the inability to directly compare across papers. We finish by providing a novel discussion of existing applications for NALU and research directions requiring further exploration.