  Recently recurrent neural networks (RNN) has been very successful in handling
sequence data. However, understanding RNN and finding the best practices for
RNN is a difficult task, partly because there are many competing and complex
hidden units (such as LSTM and GRU). We propose a gated unit for RNN, named as
Minimal Gated Unit (MGU), since it only contains one gate, which is a minimal
design among all gated hidden units. The design of MGU benefits from evaluation
results on LSTM and GRU in the literature. Experiments on various sequence data
show that MGU has comparable accuracy with GRU, but has a simpler structure,
fewer parameters, and faster training. Hence, MGU is suitable in RNN's
applications. Its simple architecture also means that it is easier to evaluate
and tune, and in principle it is easier to study MGU's properties theoretically
and empirically.
