神经自动翻译和统计自动翻译：体验感和生产率
摘要
与统计机器翻译（SAT）的结果相比，机器翻译的领域已经随着神经机器翻译（TAN）系统的发展而完全改变。因此，有必要审查最终用户（译者）对其的使用和体验感。这项工作的主要目的是确定一组译者在使用统计机器翻译系统和神经机器翻译系统在时间因素以及修改次数方面的体验感以及其的生产效率。为此，借助TAUS动态质量框架（DQF）平台，十名专业翻译首先评估了两段原始机器翻译片段——一段是指导手册，另一段是营销文本，这两段的翻译均是借助Microsoft翻译（TAE）引擎以及谷歌神经机器翻译（TAN）引擎完成的。 随后，十位翻译中的六位对两个生产效率测试进行了译后编辑，以确定编辑时间以及编辑距离。研究结果表明，在翻译人员看来，神经机器翻译引擎的效率更高，因为根据他们的使用体验感，它的译后编辑花费他们相对较少的时间，并且不需要大量修改。但是，将这些结果与在生产效率测试中获得的结果进行比较时，可以观察到，尽管谷歌神经机器翻译系统的编辑距离比Microsoft翻译引擎短，但神经机器翻译系统的译后编辑时间要比后者长得多。关键词
神经机器翻译；统计机器翻译；编辑距离；生产率；体验感；译后编辑参考
Aranberri, N. (2014). Posedición, productividad y calidad. Tradumàtica: Tecnologies de la Traducció, n. 12, pp. 471–477. <http://revistes.uab.cat/tradumatica/article/view/n12-aranberri>. <https://doi.org/10.5565/rev/tradumatica.62>
Bentivogli, L.; Bisazza, A.; Cettolo, M.; Federico, M. (2016). Neural versus Phrase-Based Machine Translation Quality: a Case Study, in: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin, Texas: Association for Computational Linguistics, pp. 257-267. <https://www.aclweb.org/anthology/D16-1025.pdf. https://doi.org/10.18653/v1/D16-1025>.
Esperança-Rodier, E.; Rossi, C.; Bérard, A.; Besacier, L. (2017). Evaluation of NMT and SMT Systems: A Study on Uses and Perceptions, in: Proceedings of the 39th Conference Translating and the Computer. London: AsLing, pp. 11-24. <https://www.semanticscholar.org/paper/Evaluation-of-NMT-and-SMT-Systems%3A-A-Study-on-Uses-Esperan%C3%A7a-Rodier-Berard/58b0ffc3892f0f24594aa424a60de0f18aab970a>.
Görög, A. (2014). Quality evaluation today: the Dynamic Quality Framework, in: Proceedings of Translating and the Computer 36: ASLING: Proceedings. Geneva: Tradulex, pp. 155-164. <http://www.tradulex.com/varia/TC36-london2014.pdf>.
Guerberof, A. (2009). Productivity and quality in MT post-editing, in: Proceedings of the Twelfth Machine Translation Summit (MT Summit XII), Beyond Translation Memories: New Tools for Translators Workshop. Ottawa, Canadá: MT Summit. <http://www.mt-archive.info/MTS-2009-Guerberof.pdf>.
Guerberof, A. (2013). What do professional translators think about post-editing?The Journal of Specialised Translation, n. 19, pp. 75–95. <http://www.jostrans.org/issue19/art_guerberof.php>.
Katan, D. (2016). Translation at the cross-roads: Time for the transcreational turn? Perspectives. Studies in Translatology, v. 24, n. 3, pp. 365–381. <https://doi.org/10.1080/0907676X.2015.1016049>
Koponen, M. (2012). Comparing human perceptions of post-editing effort with post-editing operations, in: Proceedings of the 7th Workshop on Statistical Machine Translation. Montreal, Canadá: Association for Computational Linguistics, pp. 181-190. <https://www.aclweb.org/anthology/W12-3123>.
Krings, H. P. (2001). Repairing texts. Kent: Kent State University Press.
Lohar, P.; Popovic, M.; Afli, H.; Way, A. (2019). A Systematic Comparison Between SMT and NMT on Translating User-Generated Content, in: Proceedings of CICLing 2019, the 20th International Conference on Computational Linguistics and Intelligent Text Processing, La Rochelle, France. <https://www.computing.dcu.ie/~away/PUBS/2019/A_Systematic_Comparison_Between_SMT_and_NMT_on_Translating_User_Generated_Content.pdf>
Moorkens, J. (2017). Under pressure: translation in times of austerity. Perspectives, v. 25, n. 3, pp. 464-477. <https://doi.org/10.1080/0907676X.2017.1285331>.
Moorkens, J.; Toral, A.; Castilho, S.; Way, A. (2018). Translators’ perceptions of literary post-editing using statistical and neural machine translation. Translation Spaces, v. 7, n. 2, pp. 240-262. <https://www.rug.nl/research/portal/publications/translators-perceptions-of-literary-postediting-using-statistical-and-neural-machine-translation (af141520-ca42-4b37-a779-a5f4b829f39e).html. https://doi.org/10.1075/ts.18014.moo>
Papineni, K.; Roukos, S.; Ward, T.; Zhu, W. J. (2002). Bleu: A Method for Automatic Evaluation of Machine Translation, in: Proceedings of 40th Annual Meeting of the Association for Computational Linguistics. Filadelfia: Association for Computational Linguistics, pp. 311-318. <https://doi.org/10.3115/1073083.1073135>.
Shterionov, D.; Nagle, P.; Casanellas, L.; Superbo, R.; O’Dowd, T. (2017). Empirical evaluation of NMT and PBSMT quality for large-scale translation production, in: Proceedings of the Annual Conference of the European Association for Machine Translation (EAMT): User Track. Praga: European Association of Machine Translation, pp. 74-79. <https://ufal.mff.cuni.cz/eamt2017/user-project-product-papers/papers/user/EAMT2017_paper_83.pdf>.
Snover, M.; Dorr, B.; Schwartz, R.; Micciulla, L.; Makhoul, J. (2006). A study of translation edit rate with targeted human annotation, in: Proceedings of the 7th Biennial Conference of the Association for Machine Translation in the Americas (AMTA-2006). Cambridge, Massachusetts: Association for Machine Translation in the Americas.
Torres-Hostench, O.; Presas, M.; Cid-Leal, P. (2016). El uso de traducción automática y posedición en las empresas de servicios lingüísticos españolas: informe de investigación ProjecTA 2015. Bellaterra, Cerdanyola del Vallès. <https://ddd.uab.cat/record/166753>.
Vilar, D.; Xu, J.; D’Haro L. F.; et al. (2006). Error analysis of statistical machine translation output, in: Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06). European Language Resources Association. Genoa, Italia, pp. 697-702. <http://www.lrec-conf.org/proceedings/lrec2006/pdf/413_pdf.pdf>.
Way, A. (2013). Traditional and Emerging Use-Cases for Machine Translation, in: Proceedings of Translating and the Computer 35. London. <https://www.computing.dcu.ie/~away/PUBS/2013/Way_ASLIB_2013.pdf>.
Wołk, K.; Koržinek, D. (2017). Comparison and Adaptation of Automatic Evaluation Metrics for Quality Assessment of Re-Speaking. Computer Science, v. 18, n. 2, pp. 129. <https://doi.org/10.7494/csci.2017.18.2.129>.
已出版
Downloads
Copyright (c) 2019 Ariana López Pereira
This work is licensed under a Creative Commons Attribution 4.0 International License.
