@InProceedings{ma-EtAl:2018:Long1,
  author    = {Ma, Chunpeng  and  Tamura, Akihiro  and  Utiyama, Masao  and  Zhao, Tiejun  and  Sumita, Eiichiro},
  title     = {Forest-Based Neural Machine Translation},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {1253--1263},
  abstract  = {Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors. For statistical machine translation (SMT), forest-based methods have been proven to be effective for solving this problem, while for NMT this kind of approach has not been attempted. This paper proposes a forest-based NMT method that translates a linearized packed forest under a simple sequence-to-sequence framework (i.e., a forest-to-sequence NMT model). The BLEU score of the proposed method is higher than that of the sequence-to-sequence NMT, tree-based NMT, and forest-based SMT systems.},
  url       = {http://www.aclweb.org/anthology/P18-1116}
}