Asynchronous Bidirectional Decoding for Neural Machine Translation

Understand

The dominant neural machine translation (NMT) models apply unified attentional encoder-decoder neural networks for translation.

• Traditionally, the NMT decoders adopt recurrent neural networks (RNNs) to perform translation in a left-toright manner, leaving the target-side contexts generated from right to left unexploited during translation.
• In this paper, we equip the conventional attentional encoder-decoder NMT framework with a backward decoder, in order to explore bidirectional decoding for NMT.
• Attending to the hidden state sequence produced by the encoder, our backward decoder first learns to generate the target-side hidden state sequence from right to left.