  Neural machine translation (NMT) systems require large amounts of high
quality in-domain parallel corpora for training. State-of-the-art NMT systems
still face challenges related to out-of-vocabulary words and dealing with
low-resource language pairs. In this paper, we propose and compare several
models for fusion of bilingual lexicons with an end-to-end trained
sequence-to-sequence model for machine translation. The result is a fusion
model with two information sources for the decoder: a neural conditional
language model and a bilingual lexicon. This fusion model learns how to combine
both sources of information in order to produce higher quality translation
output. Our experiments show that our proposed models work well in relatively
low-resource scenarios, and also effectively reduce the parameter size and
training cost for NMT without sacrificing performance.
