XNMT: the eXtensible Neural Machine Translation toolkit

Graham Neubig, Matthias Sperber, Xinyi Wang, Matthieu Felix, Austin Matthews, Sarguna Padmanabhan, Ye Qi, Devendra Sachan, Philip Arthur, Pierre Godard, John Hewitt, Rachid Riad, Liming Wang

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

Abstract

This paper describes XNMT, the eXtensible Neural Machine Translation toolkit. XNMT distinguishes itself from other open-source NMT toolkits by its focus on modular code design, with the purpose of enabling fast iteration in research and replicable, reliable results. In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing. XNMT is available open-source at https://github.com/neulab/xnmt.
Original languageEnglish
Title of host publicationThe 13th Conference of The Association for Machine Translation in the Americas - Proceedings
Subtitle of host publicationVol. 1: MT Researchers’ Track
EditorsColin Cherry, Graham Neubig
Place of PublicationBoston MA USA
PublisherAssociation for Machine Translation in the Americas
Pages185-192
Number of pages8
Publication statusPublished - Mar 2018
EventConference of the Association for Machine Translation in the Americas 2018 - Boston, United States of America
Duration: 17 Mar 201821 Mar 2018
Conference number: 13th
http://www.conference.amtaweb.org/

Conference

ConferenceConference of the Association for Machine Translation in the Americas 2018
Abbreviated titleATMA 2018
Country/TerritoryUnited States of America
CityBoston
Period17/03/1821/03/18
Internet address

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