Multi-Source Syntactic Neural Machine Translation

Anna Currey, Kenneth Heafield

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

Abstract

We introduce a novel multi-source technique for incorporating source syntax into neural machine translation using linearized parses. This is achieved by employing separate encoders for the sequential and parsed versions of the same source sentence; the resulting representations are then combined using a hierarchical attention mechanism. The proposed model improves over both seq2seq and parsed baselines by over 1 BLEU on the WMT17 English!German task. Further analysis shows that our multi-source syntactic model is able to translate successfully without any parsed input, unlike standard parsed methods. In addition, performance does not deteriorate as much on long sentences as for the baselines.
Original languageEnglish
Title of host publicationProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Place of PublicationBrussels, Belgium
PublisherAssociation for Computational Linguistics
Pages2961-2966
Number of pages6
Publication statusPublished - Nov 2018
Event2018 Conference on Empirical Methods in Natural Language Processing - Square Meeting Center, Brussels, Belgium
Duration: 31 Oct 20184 Nov 2018
http://emnlp2018.org/

Conference

Conference2018 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2018
CountryBelgium
CityBrussels
Period31/10/184/11/18
Internet address

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