Abstract
The development of machine translation (MT) has been successful in breaking the language barrier of the world’s top 10-20 languages. However, for the rest of it, delivering an acceptable translation quality is still a challenge due to the limited resource. To tackle this problem, most studies focus on augmenting data while overlooking the fact that we can borrow high-quality natural data from the closely-related language. In this work, we propose an MT model training strategy by increasing the language directions as a means of augmentation in a multilingual setting. Our experiment result using Indonesian and Malaysian on the state-of-the-art MT model showcases the effectiveness and robustness of our method.- Anthology ID:
- 2022.loresmt-1.11
- Volume:
- Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Atul Kr. Ojha, Chao-Hong Liu, Ekaterina Vylomova, Jade Abbott, Jonathan Washington, Nathaniel Oco, Tommi A Pirinen, Valentin Malykh, Varvara Logacheva, Xiaobing Zhao
- Venue:
- LoResMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 84–92
- Language:
- URL:
- https://aclanthology.org/2022.loresmt-1.11
- DOI:
- Cite (ACL):
- Alberto Poncelas and Johanes Effendi. 2022. Benefiting from Language Similarity in the Multilingual MT Training: Case Study of Indonesian and Malaysian. In Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022), pages 84–92, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- Benefiting from Language Similarity in the Multilingual MT Training: Case Study of Indonesian and Malaysian (Poncelas & Effendi, LoResMT 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.loresmt-1.11.pdf
- Data
- CCMatrix