文章基本信息

标题：Hierarchical Phrase-Based Translation with Weighted Finite-State Transducers and Shallow-n Grammars
本地全文：下载
作者：Adrià de Gispert ; Gonzalo Iglesias ; Graeme Blackwood 等
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2010
卷号：36
期号：3
页码：505-533
DOI：10.1162/coli_a_00006
语种：English
出版社：MIT Press
摘要：In this article we describe HiFST, a lattice-based decoder for hierarchical phrase-based translation and alignment. The decoder is implemented with standard Weighted Finite-State Transducer (WFST) operations as an alternative to the well-known cube pruning procedure. We find that the use of WFSTs rather than k-best lists requires less pruning in translation search, resulting in fewer search errors, better parameter optimization, and improved translation performance. The direct generation of translation lattices in the target language can improve subsequent rescoring procedures, yielding further gains when applying long-span language models and Minimum Bayes Risk decoding. We also provide insights as to how to control the size of the search space defined by hierarchical rules. We show that shallow-n grammars, low-level rule catenation, and other search constraints can help to match the power of the translation system to specific language pairs.