BTG-based machine translation with simple reordering model using structured perceptron

Hao Wang, Yves Lepage

研究成果: Paper

抜粋

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.

元の言語English
ページ114-123
ページ数10
出版物ステータスPublished - 2019 1 1
イベント31st Pacific Asia Conference on Language, Information and Computation, PACLIC 2017 - Cebu City, Philippines
継続期間: 2017 11 162017 11 18

Conference

Conference31st Pacific Asia Conference on Language, Information and Computation, PACLIC 2017
Philippines
Cebu City
期間17/11/1617/11/18

    フィンガープリント

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)

これを引用

Wang, H., & Lepage, Y. (2019). BTG-based machine translation with simple reordering model using structured perceptron. 114-123. 論文発表場所 31st Pacific Asia Conference on Language, Information and Computation, PACLIC 2017, Cebu City, Philippines.