基于门控循环神经网络词性标注的蒙汉机器翻译研究

统计机器翻译可以通过统计方法预测出目标词,但没有充分理解原文语义关系,因而得到的译文质量不高。针对该问题,利用一种基于门控单元循环神经网络结构来对蒙汉神经机器翻译系统进行建模,引入注意力机制来获取双语词语的对齐信息,并在构建字典过程中对双语词语进行词性标注来强化语义,以此来缓解因欠训练导致的错译问题。实验结果表明,与RNN的基准系统和传统的统计机器翻译方法相比,该方法BLEU值得到一定的提升。

Mongolian-Chinese Machine Translation Research Based on Part of Speech Tagging with Gated Unit Neural Network

Statistics machine translation may be able to predict a relatively accurate target word with statistical analysis method, but it cannot get a much better translation as it couldn’t fully understand the original semantic relations. To address this problem, the model of Mongolian-Chinese machine translation system is constructed using gated unit recurrent neural network structure, and introduce the global attention mechanism to obtain bilingual alignment information. In the process of constructing a dictionary, the bilingual words are annotated to strengthen the semantics, alleviating the problem caused by erroneous training. The research result shows that the BLEU value is certainly promoted and improved compared with previous benchmark research and traditional statistical machine translation method.