@InProceedings{long-EtAl:2016:WAT2016,
  author    = {Long, Zi  and  Utsuro, Takehito  and  Mitsuhashi, Tomoharu  and  Yamamoto, Mikio},
  title     = {Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine Translation},
  booktitle = {Proceedings of the 3rd Workshop on Asian Translation (WAT2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {47--57},
  abstract  = {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.},
  url       = {http://aclweb.org/anthology/W16-4602}
}