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 leads to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.
@article{arxiv.2004.05219,
title = {Joint translation and unit conversion for end-to-end localization},
author = {Georgiana Dinu and Prashant Mathur and Marcello Federico and Stanislas Lauly and Yaser Al-Onaizan},
journal= {arXiv preprint arXiv:2004.05219},
year = {2020}
}