August, 15, 2017

What We’re Reading: Domain Attention with an Ensemble of Experts

1 Minute Read

A major problem in effective deployment of machine learning systems in practice is domain adaptation — given a large auxiliary supervised dataset and a smaller dataset of interest, using the auxiliary dataset to increase performance on the smaller dataset. This paper considers the case where we have K datasets from distinct domains and adapting quickly to a new dataset. It learns K separate models on each of the K datasets and treats each as experts. Then given a new domain it creates another model for this domain, but in addition, computes attention over the experts. It computes attention via a dot product that computes the similarity of the new domain’s hidden representation with the other K domains’ representations.

July, 26, 2017

Technology for Interactive MT

2 Minute Read

This article describes the technology behind Lilt’s interactive translation suggestions. The details were first published in an academic conference paper, Models and Inference for Prefix-Constrained Machine Translation. Machine translation systems can translate whole sentences or documents, but they can also be used to finish translations that were started by a person — a form of autocomplete at the sentence level. In the computational linguistics literature, predicting the rest of a sentence is called prefix-constrainedmachine translation. The prefix of a sentence is the portion authored by a translator. A suffix is suggested by the machine to complete the translation. These suggestions are proposed interactively to translators after each word they type. Translators can accept all or part of the proposed suffix with a single keystroke, saving time by automating the most predictable parts of the translation process.