Active Selection of Data for Machine Translation

主动选择机器翻译数据

• 批准号: 0713292
• 负责人: Lorraine Levin
• 金额: --
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713292&HistoricalAwards=false
• 关键词: Active; Selection; Data; Machine; Translation

Current methods for machine translation (MT) rely on large amounts of text data. However, large data is not available for many languages or for specialized vocabularies even in major languages. This project elicits bilingual data from a fairly naive human bilingual informant.Bilingual speakers are available for a language even when large data and trained linguists are not. A Corpus Navigator uses knowledge from language typology to choose the pieces of data that are most valuable for automatic learning of MT rules. The Corpus Navigator employs active learning in the sense that its state is updated by eliciting data from a human translator.Two hypotheses are being tested: an MT system can get by with less data if it is the right data, and that the right data can be acquired through an active learning process guided by linguistic knowledge.Current government-run MT evaluations provide a testbed for these hypotheses. The outputs of MT systems trained on different data sets are compared in order to determine whether the hypotheses are correct. An initial prototype Corpus Navigator is being produced as a proof-of-concept. This project will make it easier to build MT systems in situations where large text resources are not available. Languages that will be tested may include Inupiaq, Bengali, Thai, Urdu, Uzbek, and Tigrinia.The output of Corpus Navigation is a parallel, word-aligned corpus annotated with a semantic feature structure. This data will be available to other researchers.

当前的机器翻译方法依赖于大量的文本数据。然而，对于许多语言，甚至对于主要语言中的专门词汇表，大数据都是不可用的。这个项目从一个相当幼稚的人类双语信息者那里得到双语数据。即使没有大数据和训练有素的语言学家，也可以使用双语者来学习一种语言。语料库导航器使用来自语言类型学的知识来选择对自动学习机器翻译规则最有价值的数据块。语料库导航器采用主动学习，它的状态是通过从人工翻译中提取数据来更新的。两个假设正在被测试：如果是正确的数据，一个机器翻译系统可以用更少的数据来完成，正确的数据可以通过语言知识指导下的主动学习过程获得。目前政府运行的MT评估为这些假设提供了一个测试平台。在不同的数据集上训练的机器翻译系统的输出进行比较，以确定假设是否正确。最初的原型语料库导航器正在生产，作为概念验证。这个项目将使在没有大量文本资源的情况下更容易构建MT系统。测试的语言可能包括因纽皮克语、孟加拉语、泰语、乌尔都语、乌兹别克语和提格里尼亚语。语料库导航的输出是一个用语义特征结构注释的平行的、词对齐的语料库。这些数据将提供给其他研究人员。