Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-Resource Language Translation
- Surangika Ranathungaa ,
- Shravan Nayak ,
- Shih-Ting Cindy Huang ,
- Yanke Mao ,
- Tong Su ,
- Yun-Hsiang Ray Chan ,
- Songchen Yuan ,
- Anthony Rinaldi ,
- Annie Lee
ACM Transactions on Asian and Low-Resource Language Information Processing | , Vol 25: pp. 1-34
Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language’s representation in the model are limited. This restricts the capabilities of domain-specific NMT systems for low-resource languages (LRLs). As a solution, parallel data from auxiliary domains can be used either to fine-tune or to further pre-train the msLM. We present an evaluation of the effectiveness of these two techniques in the context of domain-specific LRL-NMT. We also explore the impact of domain divergence on NMT model performance. We recommend several strategies for utilizing auxiliary parallel data in building domain-specific NMT models for LRLs.