Multilingual Neural Machine Translation with Task-Specific Attention

Understand

Multilingual machine translation addresses the task of translating between multiple source and target languages.

• We propose task-specific attention models, a simple but effective technique for improving the quality of sequence-to-sequence neural multilingual translation.
• Our approach seeks to retain as much of the parameter sharing generalization of NMT models as possible, while still allowing for language-specific specialization of the attention model to a particular language-pair or task.
• Our experiments on four languages of the Europarl corpus show that using a target-specific model of attention provides consistent gains in translation quality for all possible translation directions, compared to a model in which all parameters are shared.