Article published in:
Audiovisual Translation: Theoretical and methodological challenges
Edited by Yves Gambier and Sara Ramos Pinto
[Target 28:2] 2016
► pp. 206–221

Machine translation quality in an audiovisual context

| DFKI, Berlin
| DFKI, Berlin
| University College London
| text&form/DFKI, Berlin
| Humboldt-Universität zu Berlin
The volume of Audiovisual Translation (AVT) is increasing to meet the rising demand for data that needs to be accessible around the world. Machine Translation (MT) is one of the most innovative technologies to be deployed in the field of translation, but it is still too early to predict how it can support the creativity and productivity of professional translators in the future. Currently, MT is more widely used in (non-AV) text translation than in AVT. In this article, we discuss MT technology and demonstrate why its use in AVT scenarios is particularly challenging. We also present some potentially useful methods and tools for measuring MT quality that have been developed primarily for text translation. The ultimate objective is to bridge the gap between the tech-savvy AVT community, on the one hand, and researchers and developers in the field of high-quality MT, on the other.
Keywords: machine translation, translation quality, evaluation, audiovisual translation
Article outline
  • 1.Introduction
  • 2.Background: Statistical Machine Translation in a nutshell
    • 2.1The challenge of assessing MT Quality
    • 2.2What MT does best and why
  • 3.Problems impacting the automatic translation of subtitles
    • 3.1Domain and genre
    • 3.2Lack of visual context
    • 3.3Oral style
    • 3.4Lack of context
  • 4.Measuring Machine Translation quality
    • 4.1Quality evaluation in MT Research
    • 4.2Multidimensional Quality Metrics (MQM)
  • 5.Summary
  • Acknowledgments
  • Notes
  • References
Published online: 09 August 2016
https://doi.org/10.1075/target.28.2.03bur
References
Avramidis, Eleftherios, Aljoscha Burchardt, Christian, Federmann, Maja Popovićs, Cindy Tscherwinka, and David Vilar
2012 “Involving Language Professionals in the Evaluation of Machine Translation.” In Proceedings of LREC 2012, 1127–1130. http://​www​.lrec​-conf​.org​/proceedings​/lrec2012​/index​.html. Accessed December 12, 2015.
Banerjee, Satanjeev, and Alon Lavie
2005 “METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments.” In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, ed. by Jade Goldstein, Alon Lavie, Chin-Yew Lin, and Clare Voss, 65–72. Michigan, MI: University of Michigan.Google Scholar
Bywood, Lindsay, Martin Volk, Mark Fishel, and Panayota Georgakopoulou
2013 “Parallel Subtitle Corpora and their Applications in Machine Translation and Translatology.” In Corpus Linguistics and AVT: in Search of an Integrated Approach, special issue of Perspectives: Studies in Translatology 21 (4): 1–16. CrossrefGoogle Scholar
Chaume, Frederic
2004 Cine y traducción. Madrid: Cátedra.Google Scholar
De Sousa, Sheila C. M., Wilker Aziz, and Lucia Specia
2011 “Assessing the Post-Editing Effort for Automatic and Semi-Automatic Translations of DVD Subtitles.” In Proceedings of the International Conference on Recent Advances in Natural Language Processing, ed. by Galia Angelova, Kalina Bontcheva, Ruslan Mitkov, and Nikolai Nikolov, 97–103. http://​www​.aclweb​.org​/anthology​/R11​-1014​.pdf. Accessed December 22, 2015.
Díaz-Cintas, Jorge, and Aline Remael
2007 Audiovisual Translation, Subtitling. Manchester: St. Jerome.Google Scholar
Etchegoyhen, Thierry, Lindsay Bywood, Mark Fishel, Panayota Georgakopoulou, Jie Jiang, Gerard van Loenhout, Arantza del Pozo, Mirjam Sepesy Maucec, Anja Turner, and Martin Volk
2014 “Machine Translation for Subtitling: A Large-Scale Evaluation.” In Proceedings of LREC 2014, 46–53. http://​www​.lrec​-conf​.org​/proceedings​/lrec2014​/index​.html. Accessed December 22, 2015.
Lommel, Arle, Aljoscha Burchardt, and Hans Uszkoreit
2014 “Multidimensional Quality Metrics (MQM): A Framework for Declaring and Describing Translation Quality Metrics.” In Tradumàtica: tecnologies de la traducció 0 (12): 455–463. CrossrefGoogle Scholar
Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu
2002 “BLEU: A Method for Automatic Evaluation of Machine Translation.” In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 311–318. http://​dl​.acm​.org​/citation​.cfm​?id​=1073083​&picked​=prox. Accessed December 22, 2015.
Popović, Maja
2011a “Hjerson: An Open Source Tool for Automatic Error Classification of Machine Translation Output.” The Prague Bulletin of Mathematical Linguistics 961: 59–68. CrossrefGoogle Scholar
2011b “Morphemes and POS Tags for N-gram Based Evaluation Metrics.” In Proceedings of the Sixth Workshop on Statistical Machine Translation, 104–107. file://​​/Users​/SRP​/Downloads​/ngrams​.pdf. Accessed December 22, 2015.
Romero-Fresco, Pablo, and Juan Martínez Pérez
2015 “Accuracy Rate in Live Subtitling – the NER Model.” In Audiovisual Translation in a Global Context: Mapping an Ever-changing Landscape, ed. by Jorge Díaz Cintas, and Rocío Baños Pinero, 28–50. London: Palgrave Macmillan. http://​hdl​.handle​.net​/10142​/141892(draft). Accessed November 4, 2015. Crossref
Rubin, Ann D.
1978 “A Theoretical Taxonomy of the Differences between Oral and Written Language.” Center for the Study of Reading Technical Report 351.Google Scholar
Shah, Kashif, Eleftherios Avramidis, Ergun Biçicic, and Lucia Specia
2013 “QuEst – Design, Implementation and Extensions of a Framework for Machine Translation Quality Estimation.” The Prague Bulletin of Mathematical Linguistics 1001: 19–30. CrossrefGoogle Scholar
Vilar, David, Jia Xu, Luis Fernando d’Haro, and Hermann Ney
2006 “Error Analysis of Statistical Machine Translation Output.” In Proceedings of LREC 2006, 697–702. file://​​/Users​/SRP​/Downloads​/2lrec06​_errorAnalysis​.pdf. Accessed December 22, 2015.
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