Forest-Based Neural Machine Translation
Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Tiejun Zhao, Eiichiro Sumita · 2018
Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors.For statistical machine translation (SMT), forestbased 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-string NMT model).The BLEU score of the proposed method is higher than that of the string-to-string NMT, treebased NMT, and forest-based SMT systems.