Chunk-Level Reordering of Source Language Sentences with Automatically Learned Rules for Statistical Machine Translation
| Citations: | 10 - 0 self |
BibTeX
@MISC{Zhang_chunk-levelreordering,
author = {Yuqi Zhang and Richard Zens and Hermann Ney},
title = {Chunk-Level Reordering of Source Language Sentences with Automatically Learned Rules for Statistical Machine Translation},
year = {}
}
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Abstract
In this paper, we describe a sourceside reordering method based on syntactic chunks for phrase-based statistical machine translation. First, we shallow parse the source language sentences. Then, reordering rules are automatically learned from source-side chunks and word alignments. During translation, the rules are used to generate a reordering lattice for each sentence. Experimental results are reported for a Chinese-to-English task, showing an improvement of 0.5%–1.8% BLEU score absolute on various test sets and better computational efficiency than reordering during decoding. The experiments also show that the reordering at the chunk-level performs better than at the POS-level. 1
