Vocabulary Selection Strategies for Neural Machine Translation

Understand

Classical translation models constrain the space of possible outputs by selecting a subset of translation rules based on the input sentence.

• Recent work on improving the efficiency of neural translation models adopted a similar strategy by restricting the output vocabulary to a subset of likely candidates given the source.
• In this paper we experiment with context and embedding-based selection methods and extend previous work by examining speed and accuracy trade-offs in more detail.
• We show that decoding time on CPUs can be reduced by up to 90% and training time by 25% on the WMT15 English-German and WMT16 English-Romanian tasks at the same or only negligible change in accuracy.