  Recurrent neural networks have shown remarkable success in modeling
sequences. However low resource situations still adversely affect the
generalizability of these models. We introduce a new family of models, called
Lattice Recurrent Units (LRU), to address the challenge of learning deep
multi-layer recurrent models with limited resources. LRU models achieve this
goal by creating distinct (but coupled) flow of information inside the units: a
first flow along time dimension and a second flow along depth dimension. It
also offers a symmetry in how information can flow horizontally and vertically.
We analyze the effects of decoupling three different components of our LRU
model: Reset Gate, Update Gate and Projected State. We evaluate this family on
new LRU models on computational convergence rates and statistical efficiency.
Our experiments are performed on four publicly-available datasets, comparing
with Grid-LSTM and Recurrent Highway networks. Our results show that LRU has
better empirical computational convergence rates and statistical efficiency
values, along with learning more accurate language models.
