MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing

Marcin Junczys-Dowmunt, Roman Grundkiewicz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes the Microsoft and University of Edinburgh submission to the Automatic Post-editing shared task at WMT2018. Based on training data and systems from the WMT2017 shared task, we re- implement our own models from the last shared task and introduce improvements based on extensive parameter sharing. Next we experiment with our implementation of dual-source transformer models and data selection for the IT domain. Our submissions decisively wins the SMT postediting sub-task establishing the new state-ofthe-art and is a very close second (or equal, 16.46 vs 16.50 TER) in the NMT sub-task. Based on the rather weak results in the NMT sub-task, we hypothesize that neural-on-neural APE might not be actually useful.
Original languageEnglish
Title of host publicationProceedings of the EMNLP 2018 Third Conference on Machine Translation (WMT18)
Place of PublicationBelgium, Brussels
PublisherAssociation for Computational Linguistics
Pages822-826
Number of pages5
Publication statusPublished - Oct 2018
EventEMNLP 2018 Third Conference on Machine Translation (WMT18) - Brussels, Belgium
Duration: 31 Oct 20181 Nov 2018
http://www.statmt.org/wmt18/

Workshop

WorkshopEMNLP 2018 Third Conference on Machine Translation (WMT18)
Abbreviated titleWMT18
CountryBelgium
CityBrussels
Period31/10/181/11/18
Internet address

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