Article published In:
Audiovisual Translation: Theoretical and methodological challengesEdited by Yves Gambier and Sara Ramos Pinto
[Target 28:2] 2016
► pp. 206–221
Edited by Yves Gambier and Sara Ramos Pinto
[Target 28:2] 2016
► pp. 206–221
Machine translation quality in an audiovisual context
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.
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
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