We propose a Name-aware Machine Translation (MT) approach which can tightly integrate name processing into MT model, by jointly annotating parallel corpora, extracting name-aware translation grammar and rules, adding name phrase table and name translation driven decoding. Additionally, we also propose a new MT metric to appropriately evaluate the translation quality of informative words, by ass...

This study seeks to investigate the quality of translation generated by a popular Machine Translation (MT) platform, viz. Google Translate (GT) in translating content Russian flight charterer’s website from English into Indonesian. To analyze produced MT, we compared original texts translated human translator and machine-translated version. The is limited readability level. serving as data were...

We present a novel system combination of machine translation and text summarization which provides high quality summary translations superior to the baseline translation of the entire document. We first use supervised learning and build a classifier that predicts if the translation of a sentence has high or low translation quality. This is a reference-free estimation of MT quality which helps u...