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 | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
265–271
Language:
URL:
https://www.aclweb.org/anthology/2020.iwslt-1.32
DOI:
10.18653/v1/2020.iwslt-1.32
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.iwslt-1.32.pdf
Video:
 http://slideslive.com/38929604