A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair

Rebecca Knowles, John Ortega, Philipp Koeh

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

Abstract

Fuzzy-match repair (FMR), which combines a human-generated translation memory (TM) with the flexibility of machine translation (MT), is one way of using MT to augment resources available to translators. We evaluate rule-based,
phrase- based, and neural MT systems as black-box sources of bilingual information for FMR. We show that FMR success varies based on both the quality of the MT system and the type of MT system being used.
Original languageEnglish
Title of host publicationProceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing
Place of PublicationBoston, MA, USA
PublisherAssociation for Machine Translation in the Americas, AMTA
Pages249-255
Number of pages7
Publication statusPublished - 21 Mar 2018
EventWorkshop on Translation Quality Estimation and Automatic Post-Editing - Boston, United States
Duration: 17 Mar 201821 Mar 2018
https://amtaweb.org/

Conference

ConferenceWorkshop on Translation Quality Estimation and Automatic Post-Editing
Abbreviated titleAMTA 2018
CountryUnited States
CityBoston
Period17/03/1821/03/18
Internet address

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