Fine-tuning pre-trained Neural Machine Translation (NMT) models is the
dominant approach for adapting to new languages and domains. However,
fine-tuning requires adapting and maintaining a separate model for each target
task. We propose a simple yet efficient approach for adaptation in NMT. Our
proposed approach consists of injecting tiny task specific adapter layers into
a pre-trained model. These lightweight adapters, with just a small fraction of
the original model size, adapt the model to multiple individual tasks
simultaneously. We evaluate our approach on two tasks: (i) Domain Adaptation
and (ii) Massively Multilingual NMT. Experiments on domain adaptation
demonstrate that our proposed approach is on par with full fine-tuning on
various domains, dataset sizes and model capacities. On a massively
multilingual dataset of 103 languages, our adaptation approach bridges the gap
between individual bilingual models and one massively multilingual model for
most language pairs, paving the way towards universal machine translation.

本研究提出了一种简单而有效的适应 NMT 模型的方法，该方法是将微小的任务特定适配层注入预训练模型，能够同时适应多个不同的任务，并在两项任务中得到了验证，理论与全面的微调相当。