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.

目前的机器翻译（MT）方法依赖于大量的文本数据。 然而，大数据对于许多语言或甚至对于主要语言的专门词汇表都不可用。 这个项目从一个相当天真的人类双语线人中提取双语数据。即使在大数据和训练有素的语言学家不存在的情况下，双语者也可以使用一种语言。 语料库导航器使用来自语言类型学的知识来选择对机器翻译规则的自动学习最有价值的数据。 语料库导航器采用主动学习的方式，通过从人工翻译中获取数据来更新其状态。两个假设正在被检验：如果数据是正确的，机器翻译系统可以使用较少的数据，并且可以通过语言知识指导的主动学习过程来获取正确的数据。目前政府运行的机器翻译评估为这些假设提供了一个测试平台。 比较在不同数据集上训练的MT系统的输出，以确定假设是否正确。 Corpus Navigator的初始原型正在制作中，作为概念验证。 该项目将使在没有大文本资源的情况下构建机器翻译系统变得更加容易。 将被测试的语言可能包括Inupiaq，孟加拉语，泰语，乌尔都语，乌兹别克语和Tigrinia。语料库导航的输出是一个并行的，单词对齐的语料库，注释了语义特征结构。 这些数据将提供给其他研究人员。