STIMULATE: Generalized Example-Based Machine Translation

STIMULATE：广义的基于示例的机器翻译

• 批准号: 9618941
• 负责人: Jaime Carbonell
• 金额: $ 72.33万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-01 至 2001-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9618941&HistoricalAwards=false
• 关键词: STIMULATE; Generalized; Example; Based; Machine

Example-based machine translation (EMBT) searches a parallel corpus of pre-translated texts for the closest match to each new sentence being translated. Traditional EBMT works well only when there is a very large relevant parallel corpus (e.g. over 200 MB). The proposed investigation extends EBMT by generalizing words into semantic equivalence classes, by syntactic canonicalization of the source and target corpora, and by composing multiple partial matches, rather than selecting a single "best" match. These new methods will be evaluated in at least Spanish-English and Korean-English machine translation. Generalized EBMT promises to produce significantly higher accuracy translations than traditional EBMT, given the same size training corpus, or alternatively produce equivalent-quality translations given an order of magnitude smaller corpus. Combining the inherently brief development cycle of EBMT with the much smaller bilingual corpus requirement, makes generalized EBMT the future technology of choice for rapid deployment of machine translation to new, possibly exotic, language pairs.

基于示例的机器翻译（EMBT）在预翻译文本的平行语料库中搜索与被翻译的每个新句子最接近的匹配项。传统的EBMT只有在有非常大的相关并行语料库（例如超过200mb）时才能很好地工作。提出的研究通过将单词泛化到语义等价类，通过源语料库和目标语料库的句法规范化，以及通过组合多个部分匹配而不是选择单个“最佳”匹配来扩展EBMT。这些新方法将至少在西班牙语-英语和韩语-英语机器翻译中进行评估。在给定相同大小的训练语料库的情况下，广义EBMT有望产生比传统EBMT更高的翻译精度，或者在给定较小的语料库的情况下产生同等质量的翻译。结合EBMT固有的短暂开发周期和更小的双语语料库需求，使得广义EBMT成为未来快速部署机器翻译到新的，可能是外来的语言对的选择技术。