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.
REF propose chunk level reordering, where reordering rules are automatically learned from source-side chunks and word alignments.
Chunk-Level Reordering of Source Language Sentences with Automatically Learned Rules for Statistical Machine Translation
SSST, NAACL-HLT 2007 / AMTA Workshop on Syntax and Structure in Statistical Translation
Computer Science