Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation

Abdul Rafae Khan, Subhadarshi Panda, Jia Xu, Lampros Flokas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

This paper describes the submission of Hunter Neural Machine Translation (NMT) to the WMT’18 Biomedical translation task from English to French. The discrepancy between training and test data distribution brings a challenge to translate text in new domains. Beyond the previous work of combining in-domain with out-of-domain models, we found accuracy and efficiency gain in combining different in-domain models. We conduct extensive experiments on NMT with transfer learning. We train on different in-domain Biomedical datasets one after another. That means parameters of the previous training serve as the initialization of the next one. Together with a pre-trained out-of-domain News model, we enhanced translation quality with 3.73 BLEU points over the baseline. Furthermore, we applied ensemble learning on training models of intermediate epochs and achieved an improvement of 4.02 BLEU points over the baseline. Overall, our system is 11.29 BLEU points above the best system of last year on the EDP 2017 test set.

Original languageEnglish
Title of host publicationShared Task Papers
Pages655-661
Number of pages7
ISBN (Electronic)9781948087810
DOIs
StatePublished - 2018
Event3rd Conference on Machine Translation, WMT 2018 at the Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 - Brussels, Belgium
Duration: 31 Oct 20181 Nov 2018

Publication series

NameWMT 2018 - 3rd Conference on Machine Translation, Proceedings of the Conference
Volume2

Conference

Conference3rd Conference on Machine Translation, WMT 2018 at the Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Country/TerritoryBelgium
CityBrussels
Period31/10/181/11/18

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