¿Pueden ayudar las características del traduccionés a los usuarios a seleccionar un sistema de TA para posedición?

  1. Nora Aranberri
Zeitschrift:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Datum der Publikation: 2020

Nummer: 64

Seiten: 93-100

Art: Artikel

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Andere Publikationen in: Procesamiento del lenguaje natural

Zusammenfassung

This work explores the possibility of using translationese features as indicators of machine translation quality for users to select an MT system for post-editing assuming that a lower level of translationese will reveal a reduced need for editing. Results reveal that translationese and automatic metrics rank systems differently, opening an avenue for further research into the information each provides.

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Informationen zur Finanzierung

This research was partially supported by the Spanish MEIC and MCIU (Unsup-NMT TIN2017-91692-EXP and DOMINO PGC2018-102041-B-I00, co-funded by EU FEDER), and the BigKnowledge project (BBVA foundation grant 2018).

Geldgeber

    • PGC2018-102041-B-I00

Bibliographische Referenzen

  • Agerri, R., J. Bermudez, and G. Rigau. 2014. Ixa pipeline: Efficient and ready to use multilingual nlp tools. In LREC, volume 2014, pages 3823–3828.
  • Baker, M. 1993. Corpus linguistics and translation studies: Implications and applications. Text and technology: In honour of John Sinclair, 233:250.
  • Baroni, M. and S. Bernardini. 2005. A new approach to the study of translationese: Machine-learning the difference between original and translated text. Literary and Linguistic Computing, 21(3):259–274.
  • Cettolo, M., C. Girardi, and M. Federico. 2012. Wit3 : Web inventory of transcribed and translated talks. In Proceedings of the 16th Conference of the EAMT, pages 261– 268, Trento, Italy, May.
  • Etchegoyhen, T., E. Mart´ınez Garcia, A. Azpeitia, G. Labaka, I. Alegria, I. Cortes Etxabe, A. Jauregi Carrera, I. Ellakuria Santos, M. Martin, and E. Calonge. 2018. Neural machine translation of basque. In Proceedings of the 21st Annual Conference of the EAMT, 28-30 May, Alacant, Spain, pages 139–148.
  • Fokkens, A., A. Soroa, Z. Beloki, N. Ockeloen, G. Rigau, W. R. Van Hage, and P. Vossen. 2014. Naf and gaf: Linking linguistic annotations. In Proceedings 10th Joint ISO-ACL SIGSEM Workshop on Interoperable Semantic Annotation, pages 9–16.
  • Green, S., J. Heer, and C. D. Manning. 2013. The efficacy of human post-editing for language translation. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 439–448. ACM.
  • Laviosa, S. 2002. Corpus-based translation studies: theory, findings, applications, volume 17. Rodopi.
  • Otegi, A., N. Ezeiza, I. Goenaga, and G. Labaka. 2016. A Modular Chain of NLP Tools for Basque. In Proceedings of the 19th International Conference of Text, Speech, and Dialogue, Brno, Czech Republic, September 12-16. pages 93–100.
  • Tirkkonen-Condit, S. 2002. Translationese - a myth or an empirical fact? a study into the linguistic identifiability of translated language. Target. International Journal of Translation Studies, 14(2):207–220.
  • Toral, A. 2019. Post-editese: an exacerbated translationese. In Proceedings of MT Summit XVII, 19-23 August, Dublin, Ireland, pages 273–281.
  • Toury, G. 2012. Descriptive translation studies and beyond: Revised edition, volume 100. John Benjamins Publishing.
  • Volansky, V., N. Ordan, and S. Wintner. 2013. On the features of translationese. Digital Scholarship in the Humanities, 30(1):98–118.
Fuente de los datos: Dialnet