Reviews: LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

This work provides a novel and effective way to reduce the number of parameters for models that require handling of large vocabularies. The large drop in model size by several orders of magnitude could effectively allow some large models to be ported to the phone, which may not have been possible previously. I find it really interesting that a single method can improve both input parameter size and output size whereas previous work on softmaxes have only tackled the output side. However, I find that some technical details are lacking and the description can be confusing in some places. In particular, I find figure 2 and the unnumbered equation after Eq 1 confusing.