# Joint translation and unit conversion for end-to-end localization

arXiv: https://arxiv.org/abs/2004.05219 · HTML: https://mlsanity.com/paper/2004.05219/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Datasheet for the Pile](https://mlsanity.com/paper/2201.07311/) (2022)
- [Isochrony-Aware Neural Machine Translation for Automatic Dubbing](https://mlsanity.com/paper/2112.08548/) (2021)

## Abstract (arXiv)

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.