Abstract
This project investigates the capabilities of Machine Translation models for generating translations at varying levels of readability, focusing on texts related to COVID-19. Whilst it is possible to automatically translate this information, the resulting text may contain specialised terminology, or may be written in a style that is difficult for lay readers to understand. So far, we have collected a new dataset with manual simplifications for English and Spanish sentences in the TICO-19 dataset, as well as implemented baseline pipelines combining Machine Translation and Text Simplification models.- Anthology ID:
- 2022.eamt-1.33
- Volume:
- Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
- Month:
- June
- Year:
- 2022
- Address:
- Ghent, Belgium
- Editors:
- Helena Moniz, Lieve Macken, Andrew Rufener, Loïc Barrault, Marta R. Costa-jussà, Christophe Declercq, Maarit Koponen, Ellie Kemp, Spyridon Pilos, Mikel L. Forcada, Carolina Scarton, Joachim Van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, Margot Fonteyne
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 287–288
- Language:
- URL:
- https://aclanthology.org/2022.eamt-1.33
- DOI:
- Cite (ACL):
- Fernando Alva-Manchego and Matthew Shardlow. 2022. Towards Readability-Controlled Machine Translation of COVID-19 Texts. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 287–288, Ghent, Belgium. European Association for Machine Translation.
- Cite (Informal):
- Towards Readability-Controlled Machine Translation of COVID-19 Texts (Alva-Manchego & Shardlow, EAMT 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.eamt-1.33.pdf