A virtuous circle: laundering translation memory data using statistical machine translation.
This study compares consistency in target texts produced using translation memory (TM) with that of target texts produced using statistical machine translation (SMT), where the SMT engine is trained on the same texts as are reused in the TM workflow. These comparisons focus specifically on noun and...
| Publicado en: | Perspectives: Studies in Translatology Vol. 22; no. 3; pp. 291 - 304 |
|---|---|
| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
2014
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=96223324&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 96223324 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 0907676X S2F jtl: Perspectives: Studies in Translatology issn: 0907676X maglogo: N pubinfo: dt: 2014 vid: 22 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 96223324 10.1080/0907676X.2013.811275 ppf: 291 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P size: 299KB tig: atl: A virtuous circle: laundering translation memory data using statistical machine translation. aug: au: Moorkens, Joss Doherty, Stephen Kenny, Dorothy O'Brien, Sharon affil: Centre for Next Generation Localisation, Centre for Translation & Textual Studies, Dublin City University, Dublin, Ireland su: Translating & interpreting Verbs Nouns Short-term memory Online databases sug: subj: Translating & interpreting Verbs Nouns Short-term memory Online databases keyword: localisation statistical machine translation translation consistency translation memory translation quality ab: This study compares consistency in target texts produced using translation memory (TM) with that of target texts produced using statistical machine translation (SMT), where the SMT engine is trained on the same texts as are reused in the TM workflow. These comparisons focus specifically on noun and verb inconsistencies, as such inconsistencies appear to be highly prevalent in TM data. The study substitutes inconsistent TM target text nouns and verbs for consistent nouns and verbs from the SMT output to test whether this results in improvements in overall TM consistency and whether an SMT engine trained on the ‘laundered’ TM data performs better than the baseline engine. Improvements were observed in both TM consistency and SMT performance, a finding that indicates the potential of this approach for improving TM/MT integration. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Perspectives: Studies in Translatology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Perspectives: Studies in Translatology holder: Taylor & Francis Ltd dt: @attributes: year: 2014 holdings: @attributes: islocal: N |
|---|