文章基本信息

标题：Efficient Word Alignment with Markov Chain Monte Carlo
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作者：Robert Östling ; Jörg Tiedemann
期刊名称：The Prague Bulletin of Mathematical Linguistics
印刷版ISSN：0032-6585
电子版ISSN：1804-0462
出版年度：2016
卷号：106
期号：1
页码：125-146
DOI：10.1515/pralin-2016-0013
语种：English
出版社：Walter de Gruyter GmbH
摘要：We present EFMARAL, a new system for efficient and accurate word alignment using a Bayesian model with Markov Chain Monte Carlo (MCMC) inference. Through careful selection of data structures and model architecture we are able to surpass the fast_align system, commonly used for performance-critical word alignment, both in computational efficiency and alignment accuracy. Our evaluation shows that a phrase-based statistical machine translation (SMT) system produces translations of higher quality when using word alignments from EFMARAL than from fast_align, and that translation quality is on par with what is obtained using GIZA++, a tool requiring orders of magnitude more processing time. More generally we hope to convince the reader that Monte Carlo sampling, rather than being viewed as a slow method of last resort, should actually be the method of choice for the SMT practitioner and others interested in word alignment.