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 assigning different weights to different words according to their importance values in a document. Experiments on Chinese-English translation demonstrated the effectiveness of our approach on enhancing the quality of overall translation, name translation and word alignment over a high-quality MT baseline 1 .
REF proposed a ''Name-aware Machine Translation" approach that jointly annotates parallel corpora and extracts name-aware translation rules during decoding.
Name-aware Machine Translation
ACL
Computer Science