AI RESEARCH
Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation
arXiv CS.CL
•
ArXi:2412.19522v2 Announce Type: replace 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.