Title: Simplified Minimal Gated Unit Variations for Recurrent Neural Networks,
Abstract: Recurrent neural networks with various types of hidden units have been used
to solve a diverse range of problems involving sequence data. Two of the most
recent proposals, gated recurrent units (GRU) and minimal gated units (MGU),
have shown comparable promising results on example public datasets. In this
paper, we introduce three model variants of the minimal gated unit (MGU) which
further simplify that design by reducing the number of parameters in the
forget-gate dynamic equation. These three model variants, referred to simply as
MGU1, MGU2, and MGU3, were tested on sequences generated from the MNIST dataset
and from the Reuters Newswire Topics (RNT) dataset. The new models have shown
similar accuracy to the MGU model while using fewer parameters and thus
lowering training expense. One model variant, namely MGU2, performed better
than MGU on the datasets considered, and thus may be used as an alternate to
MGU or GRU in recurrent neural networks.