  Recurrent neural networks (RNNs) have been widely used to deal with sequence
learning problems. The input-dependent transition function, which folds new
observations into hidden states to sequentially construct fixed-length
representations of arbitrary-length sequences, plays a critical role in RNNs.
Based on single space composition, transition functions in existing RNNs often
have difficulty in capturing complicated long-range dependencies. In this
paper, we introduce a new Multi-zone Unit (MZU) for RNNs. The key idea is to
design a transition function that is capable of modeling multiple space
composition. The MZU consists of three components: zone generation, zone
composition, and zone aggregation. Experimental results on multiple datasets of
the character-level language modeling task and the aspect-based sentiment
analysis task demonstrate the superiority of the MZU.
