Multilingual Unsupervised NMT using Shared Encoder and Language-Specific Decoders

Sukanta Sen, Kamal Kumar Gupta, Asif Ekbal, Pushpak Bhattacharyya

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

Abstract

In this paper, we propose a multilingual unsupervised NMT scheme which jointly trains multiple languages with a shared encoder and multiple decoders. Our approach is based on denoising autoencoding of each language and back-translating between English and multiple non-English languages. This results in a universal encoder which can encode any language participating in training into an inter-lingual representation, and language-specific decoders. Our experiments using only monolingual corpora show that multilingual unsupervised model performs better than the separately trained bilingual models achieving improvement of up to 1.48 BLEU points on WMT test sets. We also observe that even if we do not train the network for all possible translation directions, the network is still able to translate in a many-to-many fashion leveraging encoder's ability to generate interlingual representation.
Original languageEnglish
Title of host publicationProceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Place of PublicationFlorence, Italy
PublisherAssociation for Computational Linguistics
Pages3083-3089
Number of pages7
ISBN (Electronic)978-1-950737-48-2
DOIs
Publication statusPublished - 28 Jul 2019
Event57th Annual Meeting of the Association for Computational Linguistics - Fortezza da Basso, Florence, Italy
Duration: 28 Jul 20192 Aug 2019
Conference number: 57
http://www.acl2019.org/EN/index.xhtml

Conference

Conference57th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2019
Country/TerritoryItaly
CityFlorence
Period28/07/192/08/19
Internet address

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