Simultaneous Translation

Liang Huang, Colin Cherry, Mingbo Ma, Naveen Arivazhagan, Zhongjun He


Abstract
Simultaneous translation, which performs translation concurrently with the source speech, is widely useful in many scenarios such as international conferences, negotiations, press releases, legal proceedings, and medicine. This problem has long been considered one of the hardest problems in AI and one of its holy grails. Recently, with rapid improvements in machine translation, speech recognition, and speech synthesis, there has been exciting progress towards simultaneous translation. This tutorial will focus on the design and evaluation of policies for simultaneous translation, to leave attendees with a deep technical understanding of the history, the recent advances, and the remaining challenges in this field.
Anthology ID:
2020.emnlp-tutorials.6
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
34–36
Language:
URL:
https://aclanthology.org/2020.emnlp-tutorials.6
DOI:
10.18653/v1/2020.emnlp-tutorials.6
Bibkey:
Cite (ACL):
Liang Huang, Colin Cherry, Mingbo Ma, Naveen Arivazhagan, and Zhongjun He. 2020. Simultaneous Translation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, pages 34–36, Online. Association for Computational Linguistics.
Cite (Informal):
Simultaneous Translation (Huang et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-tutorials.6.pdf