SemiAdapt and SemiLoRA: Efficient Domain Adaptation for Transformer-based Low-Resource Language Translation with a Case Study on Irish

TL;DR

This paper introduces SemiAdapt and SemiLoRA, innovative semi-supervised, parameter-efficient methods that significantly improve domain adaptation in low-resource neural machine translation, especially for Irish, while reducing computational costs.

Contribution

The paper presents SemiAdapt and SemiLoRA, novel semi-supervised approaches that enhance domain adaptation in NMT using PEFT, outperforming traditional fine-tuning methods in low-resource settings.

Findings

SemiAdapt outperforms full-domain fine-tuning.

SemiLoRA matches or exceeds full-model fine-tuning performance.

Embedding-based inference excels on larger, noisier datasets.

Abstract

Fine-tuning is widely used to tailor large language models for specific tasks such as neural machine translation (NMT). However, leveraging transfer learning is computationally expensive when fine-tuning large multilingual models with billions of parameters, thus creating a barrier to entry for researchers working on low-resource domains such as Irish translation. Parameter-efficient fine-tuning (PEFT) bridges this gap by training on a fraction of the original model parameters, with the Low-Rank Adaptation (LoRA) approach introducing small, trainable adapter layers. We introduce SemiAdapt and SemiLoRA as semi-supervised inference-efficient approaches that strengthen domain adaptation and lead to improved overall performance in NMT. We demonstrate that SemiAdapt can outperform full-domain fine-tuning, while most notably, SemiLoRA can propel PEFT methods to match or even outperform full-model fine-tuning. We further evaluate domain-by-dataset fine-tuning and demonstrate that our embedding-based inference methods perform especially well on larger and noisier corpora. All Irish translation models developed in this work are released as open resources. These methods aim to make high-quality domain adaptation and fine-tuning more accessible to researchers working with low-resource languages.