Compact Personalized Models for Neural Machine Translation
This enables space- and time-efficient personalized machine translation, though it is incremental as it builds on existing self-attentive models.
The paper tackles the problem of efficiently adapting neural machine translation models to specific domains by freezing most parameters during adaptation using group lasso regularization, achieving minimal quality loss while reducing computational costs.
We propose and compare methods for gradient-based 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.