  Neural machine translation (NMT) has significantly improved the quality of
automatic translation models. One of the main challenges in current systems is
the translation of rare words. We present a generic approach to address this
weakness by having external models annotate the training data as Experts, and
control the model-expert interaction with a pointer network and reinforcement
learning. Our experiments using phrase-based models to simulate Experts to
complement neural machine translation models show that the model can be trained
to copy the annotations into the output consistently. We demonstrate the
benefit of our proposed framework in outof-domain translation scenarios with
only lexical resources, improving more than 1.0 BLEU point in both translation
directions English to Spanish and German to English
