Paper:
We propose a solution for evaluation of mathematical expression. However, instead of designing a single end-to-end model we propose a Lego bricks style architecture. In this architecture instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one specific operation and acting a single lego brick. More difficult or complex task can then be solved using a combination of these smaller network. In this work we first identify 8 fundamental operations that are commonly used to solve arithmetic operations (such as 1 digit multiplication, addition, subtraction, sign calculator etc). These fundamental operations are then learned using simple feed forward neural networks. We then shows that different operations can be designed simply by reusing these smaller networks. As an example we reuse these smaller networks to develop larger and a more complex network to solve n-digit multiplication and n-digit division. This bottom-up strategy not only introduces reusability, we also show that it allows to generalize for computations involving n-digits and we show results for up to 7 digit numbers. Unlike existing methods, our solution also generalizes for both positive as well as negative numbers.

Review:
The paper proposes a method to design a NN based mathematical expression evaluation engine. I find that the paper could benefit a lot from some rewriting as it is not very clear and over claiming at points.
The introduction states that almost all ANNs lack generalization, this is in my opinion an overstatement. Domain shift and adaptation are techniques to cope with situations where the test data distribution is not coherent with the training data distribution. If this would be true in general we would have not seen such a resurgence and widespread use of ANN in the past years.
The paper lacks also proper citations to previous work and I find the background section and motivation rather weak. 
The fundamental operations presented in section 3 do not involve any learning at all, contrary to the referenced work of Trask et al where parameters are actually learned (see relaxation of sign function with tanh etc.). I therefore find the use of ANN as basic constituent of the block to be wrong, each network has fixed hand-crafted weights. If I were to replace ANN with the ordinary corresponding function nothing would in the presented framework.
Multiplication and division as explained in the algorithms do not require learning at all. I am afraid the ML contribution of this work is in my opinion almost non existent. I see every component as being scripted rather than learned from the data, which would be of course much more interesting.
Experiments are not clear at all, setup and explanation of results are not sufficient and I find them not thoroughly executed. Table 1 mentioned classification but the task is never clearly explained. Experiment 2 compares to NALU but in the proposed work nothing is learned, unless I misunderstood the work entirely.