Efficient Machine Translation Domain Adaptation
Pedro Martins, Zita Marinho, André F. T. Martins
Abstract
Machine translation models struggle when translating out-of-domain text, which makes domain adaptation a topic of critical importance. However, most domain adaptation methods focus on fine-tuning or training the entire or part of the model on every new domain, which can be costly. On the other hand, semi-parametric models have been shown to successfully perform domain adaptation by retrieving examples from an in-domain datastore A drawback of these retrievalaugmented models, however, is that they tend to be substantially slower. In this paper, we explore several approaches to speed up nearest neighbor machine translation. We adapt the methods recently proposed by He et al. ( Translation quality and runtimes for several domains show the effectiveness of the proposed solutions. 1