In this paper, we present a novel statistical machine translation method which employs a BTG-based reordering model during decoding. BTG-based reordering models for preordering have been widely explored, aiming to improve the standard phrase-based statistical machine translation system. Less attention has been paid to incorporating such a reordering model into decoding directly. Our reordering model differs from previous models built using a syntactic parser or directly from annotated treebanks. Here, we train without using any syntactic information. The experiment results on an English-Japanese translation task show that our BTG-based decoder achieves comparable or better performance than the more complex state-of-the-art SMT decoders.
|出版ステータス||Published - 2019|
|イベント||31st Pacific Asia Conference on Language, Information and Computation, PACLIC 2017 - Cebu City, Philippines|
継続期間: 2017 11 16 → 2017 11 18
|Conference||31st Pacific Asia Conference on Language, Information and Computation, PACLIC 2017|
|Period||17/11/16 → 17/11/18|
ASJC Scopus subject areas
- コンピュータ サイエンス（その他）