  Neural machine translation (NMT), a new approach to machine translation, has
achieved promising results comparable to those of traditional approaches such
as statistical machine translation (SMT). Despite its recent success, NMT
cannot handle a larger vocabulary because training complexity and decoding
complexity proportionally increase with the number of target words. This
problem becomes even more serious when translating patent documents, which
contain many technical terms that are observed infrequently. In NMTs, words
that are out of vocabulary are represented by a single unknown token. In this
paper, we propose a method that enables NMT to translate patent sentences
comprising a large vocabulary of technical terms. We train an NMT system on
bilingual data wherein technical terms are replaced with technical term tokens;
this allows it to translate most of the source sentences except technical
terms. Further, we use it as a decoder to translate source sentences with
technical term tokens and replace the tokens with technical term translations
using SMT. We also use it to rerank the 1,000-best SMT translations on the
basis of the average of the SMT score and that of the NMT rescoring of the
translated sentences with technical term tokens. Our experiments on
Japanese-Chinese patent sentences show that the proposed NMT system achieves a
substantial improvement of up to 3.1 BLEU points and 2.3 RIBES points over
traditional SMT systems and an improvement of approximately 0.6 BLEU points and
0.8 RIBES points over an equivalent NMT system without our proposed technique.
