Leveraging Auxiliary Domain Parallel Data in Intermediate Task Fine-tuning for Low-resource Translation

Shravan Nayak; Surangika Ranathunga; Sarubi Thillainathan; Rikki Hung; Anthony Rinaldi; Yining Wang; Jonah Mackey; Andrew Ho; En-Shiun Annie Lee

arXiv · 2306.01382

Leveraging Auxiliary Domain Parallel Data in Intermediate Task Fine-tuning for Low-resource Translation

Shravan Nayak·Surangika Ranathunga·Sarubi Thillainathan·Rikki Hung·Anthony Rinaldi·Yining Wang·Jonah Mackey·Andrew Ho·En-Shiun Annie Lee

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Abstract

NMT systems trained on Pre-trained Multilingual Sequence-Sequence (PMSS) models flounder when sufficient amounts of parallel data is not available for fine-tuning. This specifically holds for languages missing/under-represented in these models. The problem gets aggravated when the data comes from different domains. In this paper, we show that intermediate-task fine-tuning (ITFT) of PMSS models is extremely beneficial for domain-specific NMT, especially when target domain data is limited/unavailable and the considered languages are missing or under-represented in the PMSS model. We quantify the domain-specific results variations using a domain-divergence test, and show that ITFT can mitigate the impact of domain divergence to some extent.

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BibTeX RIS

Shravan Nayak, Surangika Ranathunga, Sarubi Thillainathan, Rikki Hung, Anthony Rinaldi, Yining Wang, Jonah Mackey, Andrew Ho, En-Shiun Annie Lee. 2023-06-02. Leveraging Auxiliary Domain Parallel Data in Intermediate Task Fine-tuning for Low-resource Translation. https://arxiv.org/abs/2306.01382

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