Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder

Understand

In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach.

• We are then able to employ attention-based NMT for many-to-many multilingual translation tasks.
• Our approach does not require any special treatment on the network architecture and it allows us to learn minimal number of free parameters in a standard way of training.
• Our approach has shown its effectiveness in an under-resourced translation scenario with considerable improvements up to 2.6 BLEU points.