  Recurrent Neural Network (RNN) has been widely applied for sequence modeling.
In RNN, the hidden states at current step are full connected to those at
previous step, thus the influence from less related features at previous step
may potentially decrease model's learning ability. We propose a simple
technique called parallel cells (PCs) to enhance the learning ability of
Recurrent Neural Network (RNN). In each layer, we run multiple small RNN cells
rather than one single large cell. In this paper, we evaluate PCs on 2 tasks.
On language modeling task on PTB (Penn Tree Bank), our model outperforms state
of art models by decreasing perplexity from 78.6 to 75.3. On Chinese-English
translation task, our model increases BLEU score for 0.39 points than baseline
model.

Computation and Language;Machine Learning;Neural and Evolutionary Computing