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
- Venue:
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.iwslt-1.32.pdf