STIMULATE: Generalized Example-Based Machine Translation

基于实例的机器翻译（EMBT）搜索预翻译文本的平行语料库，以找到与每个新翻译句子最接近的匹配。 传统的EBMT只有在有非常大的相关并行语料库（例如超过200 MB）时才能很好地工作。 拟议的调查扩展EBMT概括的话 成 语义 等价 班级， 通过 句法规范化的源和目标语料库，并通过组成多个部分匹配，而不是选择一个单一的“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。 广义 EBMT承诺将显著提高 在相同规模的训练语料库下，比传统EBMT更高的准确度翻译，或者产生同等质量的翻译 如果数量级比 语料库 结合EBMT固有的简短开发周期与更小的双语语料库的要求，使广义EBMT的机器翻译快速部署到新的，可能是异国情调的，语言对的未来技术的选择。

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.