Abstract
Modern machine translation models and language models are able to translate without having been trained on parallel data, greatly expanding the set of languages that they can serve. However, these models still struggle in a variety of predictable ways, a problem that cannot be overcome without at least some trusted bilingual data. This work expands on a cheap and abundant resource to combat this problem: bilingual lexica. We test the efficacy of bilingual lexica in a real-world set-up, on 200-language translation models trained on web-crawled text. We present several findings: (1) using lexical data augmentation, we demonstrate sizable performance gains for unsupervised translation; (2) we compare several families of data augmentation, demonstrating that they yield similar improvements, and can be combined for even greater improvements; (3) we demonstrate the importance of carefully curated lexica over larger, noisier ones, especially with larger models; and (4) we compare the efficacy of multilingual lexicon data versus human-translated parallel data. Based on results from (3), we develop and open-source GATITOS, a high-quality, curated dataset in 168 tail languages, one of the first human-translated resources to cover many of these languages.- Anthology ID:
- 2023.emnlp-main.26
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 371–405
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.26
- DOI:
- 10.18653/v1/2023.emnlp-main.26
- Cite (ACL):
- Alexander Jones, Isaac Caswell, Orhan Firat, and Ishank Saxena. 2023. GATITOS: Using a New Multilingual Lexicon for Low-resource Machine Translation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 371–405, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- GATITOS: Using a New Multilingual Lexicon for Low-resource Machine Translation (Jones et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.emnlp-main.26.pdf