Neural MT systems generate translations one word at a time. They can still generate fluid translations because they choose each word based on all of the words generated so far. Typically, these systems are just trained to generate the next word correctly, based on all previous words. One systematic problem with this word-by-word approach to training and translating is that the translations are often too short and omit important content. In the paper Neural Machine Translation with Reconstruction, the authors describe a clever new way to train and translate. During training, their system is encouraged not only to generate each next word correctly but also to correctly generate the original source sentence based on the translation that was generated. In this way, the model is rewarded for generating a translation that is sufficient to describe all of the content in the original source.
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.