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 (Khandelwal et al., 2021). A drawback of these retrieval-augmented models, however, is that they tend to be substantially slower. In this paper, we explore several approaches to speed up nearest neighbors machine translation. We adapt the methods recently proposed by He et al. (2021) for language modeling, and introduce a simple but effective caching strategy that avoids performing retrieval when similar contexts have been seen before. Translation quality and runtimes for several domains show the effectiveness of the proposed solutions.

DeepSPIN is a research project funded by the European Research Council (ERC) whose goal is to develop new neural structured prediction methods, models, and algorithms for improving the quality, interpretability, and data-efficiency of natural language processing (NLP) systems, with special emphasis on machine translation and quality estimation. We describe in this paper the latest findings from this project.

In the present paper we report on the development of a cluster of web services of language technology for Portuguese that we named as LXService. These web services permit the direct interaction of client applications with language processing tools via the Internet. This way of making available language technology was motivated by the need of its integration in an eLearning environment. In particular, it was motivated by the development of new multilingual functionalities that were aimed at extending a Learning Management System and that needed to resort to the outcome of some of those tools in a distributed and remote fashion. This specific usage situation happens however to be representative of a typical and recurrent set up in the utilization of language processing tools in different settings and projects. Therefore, the approach reported here offers not only a solution for this specific problem, which immediately motivated it, but contributes also some first steps for what we see as an important paradigm shift in terms of the way language technology can be distributed and find a better way to unleash its full potential and impact.