ANVITA-African: A Multilingual Neural Machine Translation System for African Languages
Pavanpankaj Vegi, Sivabhavani J, Biswajit Paul, Prasanna K R, Chitra Viswanathan
Abstract
This paper describes ANVITA African NMT system submitted by team ANVITA for WMT 2022 shared task on Large-Scale Machine Translation Evaluation for African Languages under the constrained translation track. The team participated in 24 African languages to English MT directions. For better handling of relatively low resource language pairs and effective transfer learning, models are trained in multilingual setting. Heuristic based corpus filtering is applied and it improved performance by 0.04-2.06 BLEU across 22 out of 24 African to English directions and also improved training time by 5x. Use of deep transformer with 24 layers of encoder and 6 layers of decoder significantly improved performance by 1.1-7.7 BLEU across all the 24 African to English directions compared to base transformer. For effective selection of source vocabulary in multilingual setting, joint and language wise vocabulary selection strategies are explored at the source side. Use of language wise vocabulary selection however did not consistently improve performance of low resource languages in comparison to joint vocabulary selection. Empirical results indicate that training using deep transformer with filtered corpora seems to be a better choice than using base transformer on the whole corpora both in terms of accuracy and training time.- Anthology ID:
- 2022.wmt-1.106
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1090–1097
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.106
- DOI:
- Cite (ACL):
- Pavanpankaj Vegi, Sivabhavani J, Biswajit Paul, Prasanna K R, and Chitra Viswanathan. 2022. ANVITA-African: A Multilingual Neural Machine Translation System for African Languages. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1090–1097, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- ANVITA-African: A Multilingual Neural Machine Translation System for African Languages (Vegi et al., WMT 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.wmt-1.106.pdf