Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/151501
Title: Does NMT make a difference when post-editing closely related languages? The case of Spanish-Catalan
Author: Alvarez Vidal, Sergi  
Oliver, Antoni  
Badia, Toni  
Citation: Alvarez, S. [Sergi], Oliver, A. [Antoni] & Badia, T. [Toni]. (2019). Does NMT make a difference when post-editing closely related languages? The case of Spanish-Catalan A Proceedings of MT Summit XVII. Volume 2: Translator, Project and User Tracks, Dublin, Irlanda 19-23 agost de 2019
Abstract: In the last years, we have witnessed an increase in the use of post-editing of machine translation (PEMT) in the translation industry. It has been included as part of the translation workflow because it increases productivity of translators. Currently, many Language Service Providers offer PEMT as a service. For many years now, (closely) related languages have been post-edited using rulebased and phrase-based machine translation (MT) systems because they present less challenges due to their morphological and syntactic similarities. Given the recent popularity of neural MT (NMT), this paper analyzes the performance of this approach compared to phrase-based statistical MT (PBSMT) on in-domain and general domain documents. We use standard automatic measures and temporal and technical effort to assess if NMT yields a real improvement when it comes to post-editing the Spanish-Catalan language pair.
Document type: info:eu-repo/semantics/conferenceObject
Issue Date: Aug-2019
Publication license: http://creativecommons.org/licenses/by-nd/.0/es/  
Appears in Collections:Conferencias

Files in This Item:
File Description SizeFormat 
2019-DoesNMT-MTSummit-Alvarez-Oliver-Badia.pdf185,86 kBAdobe PDFThumbnail
View/Open
Share:
Export:
View statistics

This item is licensed under aCreative Commons License Creative Commons