{"title":"基于摩西语的汉维统计机器翻译系统","authors":"Xinghua Dong, Yating Yang, Xiaoping Zhou, Junlin Zhou","doi":"10.1109/YCICT.2010.5713076","DOIUrl":null,"url":null,"abstract":"This paper is an initial explore to Chinese-Uyghur statistical machine translation. We experimented on three systems, which are based on moses framework, on Uyghur word-level corpus and morpheme-level corpus, then combine their 1-best translation hypothesis by constructing consensus network using TER alignment. The experiments show the combination of hypothesis that are from different systems can obtain a better result.","PeriodicalId":179847,"journal":{"name":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Moses-based Chinese-Uyghur statistical machine translation systems\",\"authors\":\"Xinghua Dong, Yating Yang, Xiaoping Zhou, Junlin Zhou\",\"doi\":\"10.1109/YCICT.2010.5713076\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is an initial explore to Chinese-Uyghur statistical machine translation. We experimented on three systems, which are based on moses framework, on Uyghur word-level corpus and morpheme-level corpus, then combine their 1-best translation hypothesis by constructing consensus network using TER alignment. The experiments show the combination of hypothesis that are from different systems can obtain a better result.\",\"PeriodicalId\":179847,\"journal\":{\"name\":\"2010 IEEE Youth Conference on Information, Computing and Telecommunications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE Youth Conference on Information, Computing and Telecommunications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/YCICT.2010.5713076\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/YCICT.2010.5713076","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Moses-based Chinese-Uyghur statistical machine translation systems
This paper is an initial explore to Chinese-Uyghur statistical machine translation. We experimented on three systems, which are based on moses framework, on Uyghur word-level corpus and morpheme-level corpus, then combine their 1-best translation hypothesis by constructing consensus network using TER alignment. The experiments show the combination of hypothesis that are from different systems can obtain a better result.
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