Year: 2017
Type: conference-paper
Abstract: 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.
Authors: Wang, Hao, 81434, Lepage, Yves, 80187, 70573608
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: China Scholarship Council
OpenAlex ID: W2889106576