RI: Small: Exploiting Bilingual Resources to Improve Monolingual Syntactic Tools

RI：小型：利用双语资源改进单语句法工具

• 批准号: 0915265
• 负责人: Dan Klein
• 金额: $ 10万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915265&HistoricalAwards=false
• 关键词: RI; Small; Exploiting; Bilingual; Resources

Natural language processing systems currently degrade when used outside of their training domains and languages. However, when text is analyzed along with translations into another language, the two languages provide powerful constraints on each other. For example, a syntactic construction which is ambiguous in one language may be unambiguous in another. We exploit such constraints by using multilingual models that capture the ways in which linguistic structures correspond between one language and another. These models are then used to accurately analyze both sides of parallel texts, which can in turn be used to train new, better, models for each language alone. Multilingual models are challenging because each language alone is complex, and the correspondences between languages can include deep syntactic and semantic restructurings. Focusing on syntactic parsing, we address these complexities with a hierarchy of increasingly complex models, each constraining the next. Our approach of multilingual analysis improves three technologies: resource projection, wherein tools for resource-rich languages are transferred to resource-poor ones, domain adaptation, wherein tools are transferred from one domain to another, and multilingual alignment, wherein correspondences between languages are extracted for use in machine translation pipelines. In addition to publishing the research results from this work, we also make freely available the multilingual modeling tools we develop.

目前，自然语言处理系统在其训练领域和语言之外使用时会出现性能下降。 然而，当分析文本并翻译成另一种语言时，这两种语言相互之间产生了强大的约束。 例如，在一种语言中模糊的句法结构在另一种语言中可能是明确的。 我们通过使用多语言模型来利用这些限制，这些模型捕获一种语言与另一种语言之间的语言结构对应的方式。 然后，这些模型用于准确分析并行文本的两侧，进而可以用于单独为每种语言训练新的、更好的模型。 多语言模型具有挑战性，因为每种语言都很复杂，并且语言之间的对应关系可能包括深层句法和语义重组。 专注于句法解析，我们通过日益复杂的模型层次结构来解决这些复杂性，每个模型都约束下一个模型。 我们的多语言分析方法改进了三项技术：资源投影（将资源丰富语言的工具转移到资源贫乏的语言）、领域适应（将工具从一个领域转移到另一个领域）以及多语言对齐（提取语言之间的对应关系）用于机器翻译管道。 除了发布这项工作的研究成果之外，我们还免费提供我们开发的多语言建模工具。