Joint Translation and Unit Conversion for End-to-end Localization

Georgiana Dinu, Prashant Mathur, Marcello Federico, Stanislas Lauly, Yaser Al-Onaizan


Abstract
A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper, we take unit conversions as an example and propose a data augmentation technique which lead to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.
Anthology ID:
2020.iwslt-1.32
Volume:
Proceedings of the 17th International Conference on Spoken Language Translation
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | IWSLT
SIG:
SIGSLT
Publisher:
Association for Computational Linguistics
Note:
Pages:
265–271
Language:
URL:
https://aclanthology.org/2020.iwslt-1.32
DOI:
10.18653/v1/2020.iwslt-1.32
Bibkey:
Cite (ACL):
Georgiana Dinu, Prashant Mathur, Marcello Federico, Stanislas Lauly, and Yaser Al-Onaizan. 2020. Joint Translation and Unit Conversion for End-to-end Localization. In Proceedings of the 17th International Conference on Spoken Language Translation, pages 265–271, Online. Association for Computational Linguistics.
Cite (Informal):
Joint Translation and Unit Conversion for End-to-end Localization (Dinu et al., IWSLT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.iwslt-1.32.pdf
Video:
 http://slideslive.com/38929604