Named-Entity Tagging and Domain adaptation for Better Customized Translation

Zhongwei Li, Xuancong Wang, Ai Ti Aw, Eng Siong Chng, Haizhou Li


Abstract
Customized translation need pay spe-cial attention to the target domain ter-minology especially the named-entities 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 in-domain adaptation can be combined to further improve customized machine translation.
Anthology ID:
W18-2407
Volume:
Proceedings of the Seventh Named Entities Workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | NEWS | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
41–46
Language:
URL:
https://aclanthology.org/W18-2407
DOI:
10.18653/v1/W18-2407
Bibkey:
Cite (ACL):
Zhongwei Li, Xuancong Wang, Ai Ti Aw, Eng Siong Chng, and Haizhou Li. 2018. Named-Entity Tagging and Domain adaptation for Better Customized Translation. In Proceedings of the Seventh Named Entities Workshop, pages 41–46, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Named-Entity Tagging and Domain adaptation for Better Customized Translation (Li et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-2407.pdf
Data
United Nations Parallel Corpus