Title: Vocabulary Selection Strategies for Neural Machine Translation
Authors: Gurvan L'Hostis, David Grangier, Michael Auli
Keywords: Natural language processing
TL;DR: Neural machine translation can reach same accuracy with a 10x speedup by pruning the vocabulary prior to decoding.
Abstract:
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. This brings the time to decode with a state of the art neural translation system to just over 140 words per seconds on a single CPU core for English-German.
Venue: Submitted to ICLR 2017