Simple, Scalable Adaptation for Neural Machine Translation (1909.08478v1)

Abstract: 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.

Overview of Simple, Scalable Adaptation for Neural Machine Translation

The paper "Simple, Scalable Adaptation for Neural Machine Translation" presents a compelling approach to adapting neural machine translation (NMT) systems efficiently. Historically, fine-tuning pre-trained NMT models has been the predominant method for targeting new languages or domains. However, this approach is resource-intensive, requiring a separate model for each task. This paper introduces a novel adaptation technique by integrating task-specific adapter layers within a pre-trained model, significantly reducing resource demands.

The proposed methodology utilizes lightweight adapters, which require only a minimal footprint of the original model size. This approach allows for simultaneous adaptation across multiple tasks, which remains a precise challenge in the translation domain where adaptation is often bespoke per language or domain. The approach is evaluated across two primary tasks: Domain Adaptation and Massively Multilingual NMT.

Experimental Evaluation and Results

The paper presents comprehensive evaluations on domain adaptation and multilingual NMT tasks.

1. Domain Adaptation: The experiments show that the adapter-based approach performs comparably to full fine-tuning across various domains, dataset sizes, and model capacities. For instance, in English-to-French domain adaptation tasks, adapters match the performance of full fine-tuning while incorporating significantly fewer additional parameters.
2. Multilingual NMT: An extensive experiment using a dataset covering 103 languages highlights the approach's scalability. Here, the model aims to bridge performance gaps between individual bilingual models and a massive multilateral model for numerous language pairs. The adapter methodology managed to achieve better adaptation results for low-resource languages without extensive regression on high-resource ones. This result is particularly significant given the challenges of balancing resource allocation among numerous language pairs within a unified model.

Implications and Future Directions

The implications of this research are multifaceted, impacting both practical real-world applications and theoretical developments in NMT and adaptation strategies. From a practical standpoint, the approach facilitates building universal NMT models capable of handling multiple languages and domains simultaneously without sacrificing performance. This has substantial real-world applications, especially for organizations requiring comprehensive, multilingual translation support while maintaining resource efficiency.

Theoretically, the successful implementation of light-weight adapters encourages further exploration into parameter-efficient adaptation strategies across various deep learning domains. Future studies may delve into optimizing adapter architecture, exploring joint fine-tuning with the frozen base model, or even extending the approach to other sequential learning tasks.

In conclusion, the paper contributes significant advancements toward efficient, scalable NMT systems. The adapter-based strategy not only alleviates the challenges of model proliferation but also sets a useful foundation for developing universal and adaptable neural systems. As the demand for more inclusive and adaptive AI solutions grows, such methodologies will undoubtedly gain prominence.