Automatic Estimation of Simultaneous Interpreter Performance

Craig Stewart, Nikolai Vogler, Junjie Hu, Jordan Boyd-Graber, Graham Neubig


Abstract
Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding. Methods to predict interpreter confidence and the adequacy of the interpreted message have a number of potential applications, such as in computer-assisted interpretation interfaces or pedagogical tools. We propose the task of predicting simultaneous interpreter performance by building on existing methodology for quality estimation (QE) of machine translation output. In experiments over five settings in three language pairs, we extend a QE pipeline to estimate interpreter performance (as approximated by the METEOR evaluation metric) and propose novel features reflecting interpretation strategy and evaluation measures that further improve prediction accuracy.
Anthology ID:
P18-2105
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
662–666
Language:
URL:
https://aclanthology.org/P18-2105
DOI:
10.18653/v1/P18-2105
Bibkey:
Cite (ACL):
Craig Stewart, Nikolai Vogler, Junjie Hu, Jordan Boyd-Graber, and Graham Neubig. 2018. Automatic Estimation of Simultaneous Interpreter Performance. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 662–666, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Automatic Estimation of Simultaneous Interpreter Performance (Stewart et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2105.pdf
Presentation:
 P18-2105.Presentation.pdf
Video:
 https://aclanthology.org/P18-2105.mp4
Code
 craigastewart/qe_sim_interp