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Multi-Domain Adaptation in Neural Machine Translation with Dynamic Sampling Strategies
In this paper, we study dynamic data selection strategies that are able to automatically re-evaluate the usefulness of data samples and to evolve a data selection policy in the course of training.
Latent Group Dropout for Multilingual and Multidomain Machine Translation
Multidomain and multilingual machine translation often rely on parameter sharing strategies, where large portions of the network are meant to capture the commonalities of the tasks at hand, while smaller parts are reserved to model the peculiarities of a language or a domain.
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