论文标题
大学电子邮件应用程序的神经机器翻译模型
Neural Machine Translation model for University Email Application
论文作者
论文摘要
机器翻译有许多应用程序，例如新闻翻译，电子邮件翻译，官方信函翻译等。商业翻译器，例如Google翻译滞后于区域词汇中，无法在输入中学习双语文本和目标语言。在本文中，在三年内，在大学使用的电子邮件中，提出了一个基于区域词汇的面向应用程序的神经机器翻译（NMT）模型。将ML-> en-> en-> ML翻译的最新序列到序列神经网络与Google Translate使用带有注意解码器的Google Translate进行了比较。与我们的模型相比，Google翻译的BLEU得分低表明，基于应用程序的区域模型更好。我们模型和Google翻译的EN-> mL的低BLEU得分表明，马来语具有与英语相对应的复杂语言功能。
Machine translation has many applications such as news translation, email translation, official letter translation etc. Commercial translators, e.g. Google Translation lags in regional vocabulary and are unable to learn the bilingual text in the source and target languages within the input. In this paper, a regional vocabulary-based application-oriented Neural Machine Translation (NMT) model is proposed over the data set of emails used at the University for communication over a period of three years. A state-of-the-art Sequence-to-Sequence Neural Network for ML -> EN and EN -> ML translations is compared with Google Translate using Gated Recurrent Unit Recurrent Neural Network machine translation model with attention decoder. The low BLEU score of Google Translation in comparison to our model indicates that the application based regional models are better. The low BLEU score of EN -> ML of our model and Google Translation indicates that the Malay Language has complex language features corresponding to English.
