Fuzzy-match repair guided by quality estimation
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http://hdl.handle.net/10045/115720
Título: | Fuzzy-match repair guided by quality estimation |
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Autor/es: | Ortega, John E. | Forcada, Mikel L. | Sánchez-Martínez, Felipe |
Grupo/s de investigación o GITE: | Transducens |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Fuzzy-match repair | Computer-aided translation | Translation memories | Quality estimation |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 2-sep-2020 |
Editor: | IEEE |
Cita bibliográfica: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022, 44(3): 1264-1277. https://doi.org/10.1109/TPAMI.2020.3021361 |
Resumen: | Computer-aided translation tools based on translation memories are widely used to assist professional translators. A translation memory (TM) consists of a set of translation units (TU) made up of source- and target-language segment pairs. For the translation of a new source segment s', these tools search the TM and retrieve the TUs (s,t) whose source segments are more similar to s'. The translator then chooses a TU and edit the target segment t to turn it into an adequate translation of s'. Fuzzy-match repair (FMR) techniques can be used to automatically modify the parts of t that need to be edited. We describe a language-independent FMR method that first uses machine translation to generate, given s' and (s,t), a set of candidate fuzzy-match repaired segments, and then chooses the best one by estimating their quality. An evaluation on three different language pairs shows that the selected candidate is a good approximation to the best (oracle) candidate produced and is closer to reference translations than machine-translated segments and unrepaired fuzzy matches (t). In addition, a single quality estimation model trained on a mix of data from all the languages performs well on any of the languages used. |
Patrocinador/es: | This work was supported by the Spanish Government through the EFFORTUNE project [TIN-2015-69632-R]. |
URI: | http://hdl.handle.net/10045/115720 |
ISSN: | 0162-8828 (Print) | 1939-3539 (Online) |
DOI: | 10.1109/TPAMI.2020.3021361 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1109/TPAMI.2020.3021361 |
Aparece en las colecciones: | INV - TRANSDUCENS - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Ortega_etal_2020_IEEE-TPAMI_accepted.pdf | Accepted Manuscript (acceso abierto) | 2,12 MB | Adobe PDF | Abrir Vista previa |
Ortega_etal_2020_IEEE-TPAMI_final.pdf | Versión final (acceso restringido) | 1,42 MB | Adobe PDF | Abrir Solicitar una copia |
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