Improving the Quality of Neural Machine Translation Through Proper Translation of Name Entities
This work addresses the specific issue of name entity translation in neural machine translation, which is incremental as it builds on existing methods with a preprocessing step.
The paper tackled the problem of improving neural machine translation quality by focusing on the proper translation of name entities, achieving high accuracy rates such as 99.86% for person names and an overall accuracy of 99.52%.
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%