Named-Entity Tagging and Domain adaptation for Better Customized Translation
Zhongwei Li, Xuancong Wang, Ai Ti Aw, Eng Siong Chng, Haizhou Li · 2018
Customized translation need pay special attention to the target domain terminology especially the namedentities for the domain.Adding linguistic features to neural machine translation (NMT) has been shown to benefit translation in many studies.In this paper, we further demonstrate that adding named-entity (NE) feature with named-entity recognition (NER) into the source language produces better translation with NMT.Our experiments show that by just including the different NE classes and boundary tags, we can increase the BLEU score by around 1 to 2 points using the standard test sets from WMT2017.We also show that adding NE tags using NER and applying indomain adaptation can be combined to further improve customized machine translation.