2,720
Views
24
CrossRef citations to date
0
Altmetric
Articles

What to expect from Neural Machine Translation: a practical in-class translation evaluation exercise

ORCID Icon
Pages 375-387 | Received 15 Aug 2017, Accepted 15 Jul 2018, Published online: 23 Jul 2018

References

  • Arthur, P., G. Neubig, and S. Nakamura. 2016. “Incorporating Discrete Translation Lexicons into Neural Machine Translation.” In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 1557–1567.
  • Bahdanau, D., K. Cho, and Y. Bengio. 2014. “Neural Machine Translation by Jointly Learning to Align and Translate.” Computing Research Repository, abs/1409.0473. Austin, TX: Association for Computational Linguistics. https://arxiv.org/abs/1409.0473.
  • Bentivogli, L., A. Bisazza, M. Cettolo, and M. Federico. 2016. “Neural versus Phrase-Based Machine Translation Quality: A Case Study.” In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), 257–267. http://arxiv.org/abs/1608.04631.
  • Bojar, O., R. Chatterjee, C. Federmann, Y. Graham, B. Haddow, M. Huck, A. Jimeno Yepes, et al. 2016. “Findings of the 2016 Conference on Machine Translation.” In Proceedings of the First Conference on Machine Translation, 131–198. Association for Computational Linguistics.
  • Bowker, L., and C. McBride. 2017. “Précis-Writing as a Form of Speed Training for Translation Students.” The Interpreter and Translator Trainer 11 (4): 259–279. doi:10.1080/1750399X.2017.1359758.
  • Burchardt, A., V. Macketanz, J. Dehdari, G. Heigold, J.-T. Peter, and P. Williams. 2017. “A Linguistic Evaluation of Rule-Based, Phrase-Based, and Neural MT Engines.” The Prague Bulletin of Mathematical Linguistics 108: 159–170. doi:10.1515/pralin-2017-0017.
  • Bureau of Labor Statistics. 2018. Occupational Outlook Handbook: Interpreters and Translators. Washington, DC: U.S. Department of Labor. https://www.bls.gov/ooh/media-and-communication/interpreters-and-translators.htm.
  • Cadwell, P., S. O’Brien, and C. S. C. Teixeira. 2017. “Resistance and Accommodation: Factors for the (Non-) Adoption of Machine Translation among Professional Translators.” Perspectives 26 (3): 301–321. doi:10.1080/0907676X.2017.1337210.
  • Castilho, S., J. Moorkens, F. Gaspari, I. Calixto, J. Tinsley, and A. Way. 2017a. “Is Neural Machine Translation the New State-of-The-Art?” The Prague Bulletin of Mathematical Linguistics 108: 109–120. doi:10.1515/pralin-2017-0013.
  • Castilho, S., J. Moorkens, F. Gaspari, R. Sennrich, V. Sosoni, P. Georgakopoulou, P. Lohar, A. Way, A. Valerio Miceli Barone, and M. Gialama. 2017b. “A Comparative Quality Evaluation of PBSMT and NMT Using Professional Translators.” In Proceedings of MT Summit 2017. Nagoya: Asia-Pacific Association for Machine Translation.
  • Cho, K., B. van Merrienboer, D. Bahdanau, and Y. Bengio. 2014. “On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.” In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, 103–111, Doha, Qatar. Computing Research Repository, abs/1409.1259.
  • Doherty, S., F. Gaspari, J. Moorkens, and S. Castilho. 2018. “On Education and Training in Translation Quality Assessment.” In Translation Quality Assessment: From Principles to Practice, edited by J. Moorkens, S. Castilho, F. Gaspari, and S. Doherty, 95–106. Berlin: Springer. doi:10.1007/978-3-319-91241-7_5.
  • Doherty, S., and D. Kenny. 2014. “The Design and Evaluation of a Statistical Machine Translation Syllabus for Translation Students.” The Interpreter and Translator Trainer 8 (2): 295–315. doi:10.1080/1750399X.2014.937571.
  • EMT Network. 2017. European Master’s in Translation Competence Framework 2017. https://ec.europa.eu/info/sites/info/files/emt_competence_fwk_2017_en_web.pdf
  • Forcada, M., and R. P. Ñeco. 1997. “Recursive Hetero-Associative Memories for Translation.” In Biological and Artificial Computation: From Neuroscience to Technology, edited by J. Mira, R. Moreno-Díaz, and J. Cabestany, 453–462. Berlin: Springer.
  • Forcada, M. 2017. “Making Sense of Neural Machine Translation.” Translation Spaces 6 (2): 291–309. doi:10.1075/ts.6.2.06for.
  • Gaspari, F., H. Almaghout, and S. Doherty. 2015. “A Survey of Machine Translation Competences: Insights for Translation Technology Educators and Practitioners.” Perspectives 23 (3): 333–358. doi:10.1080/0907676X.2014.979842.
  • Guerberof, A. 2012. “Productivity and Quality in the Post-Editing of Outputs from Translation Memories and Machine Translation.” PhD diss.Universitat Rovira i Virgili.
  • Hassan, H., A. Aue, C. Chen, V. Chowdhary, J. Clark, C. Federmann, X. Huang, et al. 2018. “Achieving Human Parity on Automatic Chinese to English News Translation.” Computing Research Repository arXiv:1803.05567v1. https://arxiv.org/abs/1803.05567.
  • Hurtado Albir, A. 2007. “Competence-Based Curriculum Design for Training Translators.” The Interpreter and Translator Trainer 1 (2): 63–195. doi:10.1080/1750399X.2007.10798757.
  • Hutchins, W. J. 1986. Machine Translation: Past, Present, Future. Chichester: Ellis Horwood.
  • Kenny, D. 2007. “Translation Memories and Parallel Corpora: Challenges for the Translation Trainer.” In Across Boundaries: International Perspectives on Translation, edited by D. Kenny and K. Ryou, 192–208. Newcastle-upon-Tyne: Cambridge Scholars Publishing.
  • Kenny, D., and S. Doherty. 2014. “Statistical Machine Translation in the Translation Curriculum: Overcoming Obstacles and Empowering Translators.” The Interpreter and Translator Trainer 8 (2): 276–294. doi:10.1080/1750399X.2014.936112.
  • Koehn, P., H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, et al. 2007. “Moses: Open Source Toolkit for Statistical Machine Translation”. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL). Prague: Association for Computational Linguistics.
  • Krings, H. P. 2001. Repairing Texts. Kent, OH: Kent State University Press.
  • Lommel, A. 2018. “The Multidimensional Quality Metrics and Dynamic Quality Framework.” In Translation Quality Assessment: From Principles to Practice, edited by J. Moorkens, S. Castilho, F. Gaspari, and S. Doherty, 109–127. Berlin: Springer. doi:10.1007/978-3-319-91241-7_6.
  • Lommel, A., and D. A. DePalma. 2016. “Post-Editing Goes Mainstream.” Common Sense Advisory Report. Boston, MA: Common Sense Advisory.
  • Massey, G. 2017. “Machine Learning: Implications for Translator Education.” In Proceedings of CIUTI Forum 2017: Short- and long-term impact of artificial intelligence on language professions. doi:10.1515/les-2017-0021.
  • Mellinger, C. D. 2017. “Translators and Machine Translation: Knowledge and Skills Gaps in Translator Pedagogy.” The Interpreter and Translator Trainer 11 (4): 280–293. doi:10.1080/1750399X.2017.1359760.
  • O’Brien, S. 2012. “Translation as Human‐Computer Interaction.” Translation Spaces 1 (1): 101–122. doi:10.1075/ts.1.05obr.
  • Olohan, M. 2007. “Economic Trends and Developments in the Translation Industry.” The Interpreter and Translator Trainer 1 (1): 37–63. doi:10.1080/1750399X.2007.10798749.
  • Plitt, M., and F. Masselot. 2010. “A Productivity Test of Statistical Machine Translation Post-Editing in A Typical Localisation Context.” The Prague Bulletin of Mathematical Linguistics 93: 7–16. doi:10.2478/v10108-010-0010-x.
  • Popović, M. 2017. “Comparing Language Related Issues for NMT and PBMT between German and English.” The Prague Bulletin of Mathematical Linguistics 108: 209–220. doi:10.1515/pralin-2017-0021.
  • Sennrich, R., B. Haddow, and A. Birch. 2016. “Neural Machine Translation by Jointly Learning to Align and Translate.” In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 1715–1725.Berlin: Association for Computational Linguistics.
  • Way, A. 2018. “Traditional and Emerging Use-Cases for Machine Translation.” In Translation Quality Assessment: From Principles to Practice, edited by J. Moorkens, S. Castilho, F. Gaspari, and S. Doherty, 159–178. Berlin: Springer. doi:10.1007/978-3-319-91241-7_8.
  • Wu, Y., M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, and M. Krikun, et al. 2016. “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.” Computing Research Repository arXiv:1609.08144. https://arxiv.org/abs/1609.08144.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.