基于多编码器多解码器的大规模维汉神经网络机器翻译模型

为提升维汉机器翻译模型的翻译能力,该文提出使用多编码器多解码器的结构,搭建大规模的维汉神经网络机器翻译模型。相比于单编码器单解码器的浅层的小模型,多编码器多解码器模型具有多个编码器,可以对源语言进行多层次、多视角的压缩表示;同时具有多个解码器,可以增强目标语言的生成能力。实验证明,在大规模的训练数据上,使用该方法搭建的大规模维汉神经网络机器翻译模型,译文质量可以大幅度地超过基于短语的统计机器翻译模型和基本的神经网络翻译模型。该文还针对维汉翻译源端语言和目标端语言的翻译单元粒度进行了实验,发现维吾尔语端使用字节对编码单元、汉语端使用字单元,可以消除对汉语分词器的依赖,做到和双端都使用字节对编码单元可比的效果。

A Large-scale Uyghur-Chinese Neural Machine Translation Model Based on Multiple Encoders and Decoders

To enhance the translation ability of Uyghur-Chinese translation model,the paper proposes a large-scale Neural Machine Translation system based on multiple encoders and decoders. Compared with the encoder-decoder based shallow model,the proposed model consists of multiple encoders to represent the source sentence in multiple perspectives and has multiple decoders to extend the generation ability of the target sentence. The experiments on the big training corpus show that the translation quality of the proposed model surpasses phrase-based Statistical Machine Translation model and the basic Neural Machine Translation model. The paper also investigates the granularity of the translation unit and reveal that it is effective to employ the Byte Pair Encoding unit for Uyghur and character unit for Chinese to avoid the Chinese word segmentor and achieve comparable performance with BPE-BPE systems.