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 En-Shiun Lee

arXiv · 2412.19522

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 En-Shiun Lee

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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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Surangika Ranathungaa, Shravan Nayak, Shih-Ting Cindy Huang, Yanke Mao, Tong Su, Yun-Hsiang Ray Chan, Songchen Yuan, Anthony Rinaldi, Annie En-Shiun Lee. 2024-12-27. Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation. https://arxiv.org/abs/2412.19522

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