STIMULATE: Generalized Example-Based Machine Translation

基于实例的机器翻译(EMBT)在预先翻译的文本的平行语料库中搜索与正在翻译的每个新句子最接近的匹配。传统的EBMT只有在有非常大的相关平行语料库(例如超过200MB)时才能很好地工作。提出的研究通过将单词概括为语义等价类，通过对源和目标语料库的句法规范化，以及通过组成多个部分匹配来扩展EBMT，而不是选择单一的“最佳”匹配。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。在相同大小的训练语料库下，广义EBMT有望产生比传统EBMT高得多的准确率的翻译，或者在语料库小一个数量级的情况下，产生同等质量的翻译。将EBMT固有的短暂开发周期与较小的双语语料库需求相结合，使广义EBMT成为快速部署到新的、可能是外来的语言对的机器翻译的未来选择技术。

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.