  In recent years, the sequence-to-sequence learning neural networks with
attention mechanism have achieved great progress. However, there are still
challenges, especially for Neural Machine Translation (NMT), such as lower
translation quality on long sentences. In this paper, we present a hierarchical
deep neural network architecture to improve the quality of long sentences
translation. The proposed network embeds sequence-to-sequence neural networks
into a two-level category hierarchy by following the coarse-to-fine paradigm.
Long sentences are input by splitting them into shorter sequences, which can be
well processed by the coarse category network as the long distance dependencies
for short sentences is able to be handled by network based on
sequence-to-sequence neural network. Then they are concatenated and corrected
by the fine category network. The experiments shows that our method can achieve
superior results with higher BLEU(Bilingual Evaluation Understudy) scores,
lower perplexity and better performance in imitating expression style and words
usage than the traditional networks.
