In this paper, we have shown a method of improving the quality of neural
machine translation by translating/transliterating name entities as a
preprocessing step. Through experiments we have shown the performance gain of
our system. For evaluation we considered three types of name entities viz
person names, location names and organization names. The system was able to
correctly translate mostly all the name entities. For person names the accuracy
was 99.86%, for location names the accuracy was 99.63% and for organization
names the accuracy was 99.05%. Overall, the accuracy of the system was 99.52%

本文介绍了一种通过将名称实体作为预处理步骤进行翻译 / 音译来提高神经机器翻译质量的方法，并进行了实验证明其性能提高。考虑了三种类型的名称实体，包括人名、地名和组织名。结果表明，该系统能够正确翻译大多数名称实体，其准确率为人名 99.86％，地名 99.63％，组织名 99.05％，总体准确率为 99.52％。

通过正确翻译命名实体来提高神经机器翻译质量

Improving the Quality of Neural Machine Translation Through Proper  Translation of Name Entities

Machine Transliteration has come out to be an emerging and a very important
research area in the field of machine translation. Transliteration basically
aims to preserve the phonological structure of words. Proper transliteration of
name entities plays a very significant role in improving the quality of machine
translation. In this paper we are doing machine transliteration for
English-Punjabi language pair using rule based approach. We have constructed
some rules for syllabification. Syllabification is the process to extract or
separate the syllable from the words. In this we are calculating the
probabilities for name entities (Proper names and location). For those words
which do not come under the category of name entities, separate probabilities
are being calculated by using relative frequency through a statistical machine
translation toolkit known as MOSES. Using these probabilities we are
transliterating our input text from English to Punjabi.

本文采用基于规则的方法对英 - 旁遮普语进行机器音译，通过计算名实体和非名实体的概率以及使用统计机器翻译工具包 MOSES 计算相对频率来提高机器翻译的质量，同时进行音节划分。