We propose and compare methods for gradientbased domain adaptation of self-attentive neural machine translation models.We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization.We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs.Our system architecture-combining a state-of-the-art self-attentive model with compact domain adaptation-provides high quality personalized machine translation that is both space and time efficient.

We propose a novel learning approach for statistical machine translation (SMT) that allows to extract supervision signals for structured learning from an extrinsic re-sponse to a translation input. We show how to generate responses by grounding SMT in the task of executing a seman-tic parse of a translated query against a database. Experiments on the GEO-QUERY database show an improvement of about 6 points in F1-score for response-based learning over learning from refer-ences only on returning the correct an-swer from a semantic parse of a translated query. In general, our approach alleviates the dependency on human reference trans-lations and solves the reachability problem in structured learning for SMT. 1