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 +4 more
12/27/2024
cs.CL

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Abstract

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.

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Cite this paper

@article{ranathungaa2024exploiting,
  title  = {Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation},
  author = {Surangika Ranathungaa and Shravan Nayak and Shih-Ting Cindy Huang and Yanke Mao and Tong Su and Yun-Hsiang Ray Chan and Songchen Yuan and Anthony Rinaldi and Annie En-Shiun Lee},
  year   = {2024},
  eprint = {2412.19522},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.19522v1}
}

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