Abstract
In this paper, we describe our three submissions to the SIGMORPHON 2020 shared task 1 on grapheme-to-phoneme conversion for 15 languages. We experimented with a single multilingual transformer model. We observed that the multilingual model achieves results on par with our separately trained monolingual models and is even able to avoid a few of the errors made by the monolingual models.- Anthology ID:
- 2020.sigmorphon-1.7
- Volume:
- Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Garrett Nicolai, Kyle Gorman, Ryan Cotterell
- Venue:
- SIGMORPHON
- SIG:
- SIGMORPHON
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 85–89
- Language:
- URL:
- https://aclanthology.org/2020.sigmorphon-1.7
- DOI:
- 10.18653/v1/2020.sigmorphon-1.7
- Cite (ACL):
- Omnia ElSaadany and Benjamin Suter. 2020. Grapheme-to-Phoneme Conversion with a Multilingual Transformer Model. In Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 85–89, Online. Association for Computational Linguistics.
- Cite (Informal):
- Grapheme-to-Phoneme Conversion with a Multilingual Transformer Model (ElSaadany & Suter, SIGMORPHON 2020)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/2020.sigmorphon-1.7.pdf