文章基本信息

标题：Source Language Adaptation Approaches for Resource-Poor Machine Translation
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作者：Pidong Wang ; Preslav Nakov ; Hwee Tou Ng 等
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2016
卷号：42
期号：2
页码：277-306
DOI：10.1162/COLI_a_00248
语种：English
出版社：MIT Press
摘要：Most of the world languages are resource-poor for statistical machine translation; still, many of them are actually related to some resource-rich language. Thus, we propose three novel, language-independent approaches to source language adaptation for resource-poor statistical machine translation. Specifically, we build improved statistical machine translation models from a resource-poor language POOR into a target language TGT by adapting and using a large bitext for a related resource-rich language RICH and the same target language TGT. We assume a small POOR–TGT bitext from which we learn word-level and phrase-level paraphrases and cross-lingual morphological variants between the resource-rich and the resource-poor language. Our work is of importance for resource-poor machine translation because it can provide a useful guideline for people building machine translation systems for resource-poor languages. Our experiments for Indonesian/Malay–English translation show that using the large adapted resource-rich bitext yields 7.26 BLEU points of improvement over the unadapted one and 3.09 BLEU points over the original small bitext. Moreover, combining the small POOR–TGT bitext with the adapted bitext outperforms the corresponding combinations with the unadapted bitext by 1.93–3.25 BLEU points. We also demonstrate the applicability of our approaches to other languages and domains.