SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages
Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, Laurent Besacier
Abstract
In recent years, multilingual machine translation models have achieved promising performance on low-resource language pairs by sharing information between similar languages, thus enabling zero-shot translation. To overcome the “curse of multilinguality”, these models often opt for scaling up the number of parameters, which makes their use in resource-constrained environments challenging. We introduce SMaLL-100, a distilled version of the M2M-100(12B) model, a massively multilingual machine translation model covering 100 languages. We train SMaLL-100 with uniform sampling across all language pairs and therefore focus on preserving the performance of low-resource languages. We evaluate SMaLL-100 on different low-resource benchmarks: FLORES-101, Tatoeba, and TICO-19 and demonstrate that it outperforms previous massively multilingual models of comparable sizes (200-600M) while improving inference latency and memory usage. Additionally, our model achieves comparable results to M2M-100 (1.2B), while being 3.6x smaller and 4.3x faster at inference.- Anthology ID:
- 2022.emnlp-main.571
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8348–8359
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.571
- DOI:
- 10.18653/v1/2022.emnlp-main.571
- Cite (ACL):
- Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, and Laurent Besacier. 2022. SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8348–8359, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages (Mohammadshahi et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.emnlp-main.571.pdf