Experiences with English-Hindi, English-Tamil and English-Kannada Transliteration Tasks at NEWS 2009

Manoj Kumar Chinnakotla, O. Damani
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引用次数: 20

Abstract

We use a Phrase-Based Statistical Machine Translation approach to Transliteration where the words are replaced by characters and sentences by words. We employ the standard SMT tools like GIZA++ for learning alignments and Moses for learning the phrase tables and decoding. Besides tuning the standard SMT parameters, we focus on tuning the Character Sequence Model (CSM) related parameters like order of the CSM, weight assigned to CSM during decoding and corpus used for CSM estimation. Our results show that paying sufficient attention to CSM pays off in terms of increased transliteration accuracies.
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英语-印地语,英语-泰米尔语和英语-卡纳达语在新闻2009音译任务的经验
我们使用基于短语的统计机器翻译方法进行音译,其中单词被字符替换,句子被单词替换。我们使用标准的SMT工具,如giz++来学习对齐,使用Moses来学习短语表和解码。除了调整标准的SMT参数外,我们还重点调整了字符序列模型(CSM)的相关参数,如CSM的顺序,解码过程中分配给CSM的权重以及用于CSM估计的语料库。我们的研究结果表明,在提高音译精度方面,对CSM给予足够的重视是有回报的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Report of NEWS 2016 Machine Transliteration Shared Task Transliteration by Bidirectional Statistical Machine Translation Analysis and Robust Extraction of Changing Named Entities NEWS 2009 Machine Transliteration Shared Task System Description: Transliteration with Letter-to-Phoneme Technology Phonological Context Approximation and Homophone Treatment for NEWS 2009 English-Chinese Transliteration Shared Task
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