  Compared to traditional statistical machine translation (SMT), neural machine
translation (NMT) often sacrifices adequacy for the sake of fluency. We propose
a method to combine the advantages of traditional SMT and NMT by exploiting an
existing phrase-based SMT model to compute the phrase-based decoding cost for
an NMT output and then using this cost to rerank the n-best NMT outputs. The
main challenge in implementing this approach is that NMT outputs may not be in
the search space of the standard phrase-based decoding algorithm, because the
search space of phrase-based SMT is limited by the phrase-based translation
rule table. We propose a soft forced decoding algorithm, which can always
successfully find a decoding path for any NMT output. We show that using the
forced decoding cost to rerank the NMT outputs can successfully improve
translation quality on four different language pairs.
