Many multi-domain neural machine translation (NMT) models achieve knowledge transfer by enforcing one encoder to learn shared embedding across domains . However, this design lacks adaptation to individual domains… To overcome this limitation, we propose a novel multi- domain NMT model using individual modules for each domain . We show this can be achieved by carefully designing multi-head dot-product attention modules for different domains, and eventually taking weighted averages of their parameters by word-level layer-wise domain proportions . Through this, we can achieve effective domain knowledge sharing and capture fine-grained domain-specific knowledge as well . Our experiments show that our proposed model outperforms existing ones in several NMT tasks. Our proposed model outranks existing ones in several of several tasks.

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Keywords : domain - multi - nmt - domains - knowledge -

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