Sarubi Thillainathan


2024

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Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation
Tong Su | Xin Peng | Sarubi Thillainathan | David Guzmán | Surangika Ranathunga | En-Shiun Lee
Findings of the Association for Computational Linguistics: NAACL 2024

Parameter-efficient fine-tuning (PEFT) methods are increasingly vital in adapting large-scale pre-trained language models for diverse tasks, offering a balance between adaptability and computational efficiency. They are important in Low-Resource Language (LRL) Neural Machine Translation (NMT) to enhance translation accuracy with minimal resources. However, their practical effectiveness varies significantly across different languages. We conducted comprehensive empirical experiments with varying LRL domains and sizes to evaluate the performance of 8 PEFT methods with in total of 15 architectures using the SacreBLEU score. We showed that 6 PEFT architectures outperform the baseline for both in-domain and out-domain tests and the Houlsby+Inversion adapter has the best performance overall, proving the effectiveness of PEFT methods.

2022

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Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?
En-Shiun Lee | Sarubi Thillainathan | Shravan Nayak | Surangika Ranathunga | David Adelani | Ruisi Su | Arya McCarthy
Findings of the Association for Computational Linguistics: ACL 2022

What can pre-trained multilingual sequence-to-sequence models like mBART contribute to translating low-resource languages? We conduct a thorough empirical experiment in 10 languages to ascertain this, considering five factors: (1) the amount of fine-tuning data, (2) the noise in the fine-tuning data, (3) the amount of pre-training data in the model, (4) the impact of domain mismatch, and (5) language typology. In addition to yielding several heuristics, the experiments form a framework for evaluating the data sensitivities of machine translation systems. While mBART is robust to domain differences, its translations for unseen and typologically distant languages remain below 3.0 BLEU. In answer to our title’s question, mBART is not a low-resource panacea; we therefore encourage shifting the emphasis from new models to new data.