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.
@article{arxiv.1705.01346,
title = {Going Wider: Recurrent Neural Network With Parallel Cells},
author = {Danhao Zhu and Si Shen and Xin-Yu Dai and Jiajun Chen},
journal= {arXiv preprint arXiv:1705.01346},
year = {2017}
}