Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation

Roman Grundkiewicz, Marcin Junczys-Dowmunt

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

Abstract

We combine two of the most popular approaches to automated Grammatical Error Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on Neural Machine Translation (NMT). The hybrid system achieves new state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC system preserves the accuracy of SMT output and, at the same time, generates more fluent sentences as it typical for NMT. Our analysis shows that the created systems are closer to reaching human-level performance than any other GEC system reported so far.
Original languageEnglish
Title of host publicationProceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
PublisherAssociation for Computational Linguistics
Pages284-290
Number of pages7
DOIs
Publication statusE-pub ahead of print - 1 Jun 2018
Event16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Hyatt Regency New Orleans Hotel, New Orleans, United States
Duration: 1 Jun 20186 Jun 2018
http://naacl2018.org/

Conference

Conference16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Abbreviated titleNAACL HLT 2018
CountryUnited States
CityNew Orleans
Period1/06/186/06/18
Internet address

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