RI: Large:Collaborative Research: Richer Representations for Machine Translation (REPS)

RI：大型：协作研究：更丰富的机器翻译表示 (REPS)

• 批准号: 0908532
• 负责人: Kevin Knight
• 金额: $ 58万
• 依托单位: University of Southern California
• 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: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0908532&HistoricalAwards=false
• 关键词: RI; Large; Collaborative; Research; Richer

Research in machine translation of human languages has made substantial progress recently, and surface patterns gleaned automatically from online bilingual texts work remarkably well for some language pairs. However, for many language pairs, the output of even the best systems is garbled, ungrammatical, and difficult to interpret. Chinese-to-English systems need particular improvement, despite the importance of this language pair, while English-to-Chinese translation, equally important for communication between individuals, is rarely studied. This project develops methods for automatically learning correspondences between Chinese and English at a semantic rather than surface level, allowing machine translation to benefit from recent work in semantic analysis of text and natural language generation. One part of this work determines what types of semantic analysis of source language sentences can best inform a translation system, focusing on analyzing dropped arguments, co-reference links, and discourse relations between clauses. These linguistic phenomena must generally be made more explicit when translating from Chinese to English. A second part of the work integrates natural language generation into statistical machine translation, leveraging generation technology to determine sentence boundaries, ordering of constituents, and production of function words that translation systems tend to get wrong. A third part develops and compares algorithms for training and decoding machine translation models defined on semantic representations. All of this research exploits newly-developed linguistic resources for semantic analysis of both Chinese and English. The ultimate benefits of improved machine translation technology are easier access to information and easier communication between individuals. This in turn leads to increased opportunities for trade, as well as better understanding between cultures. This project's systems for both Chinese-to-English and English-to-Chinese are developed with the expectation that the approaches will be applied to other language pairs in the future.

人类语言的机器翻译研究最近取得了实质性的进展，从在线双语文本中自动收集的表面模式对于某些语言对非常有效。然而，对于许多语言对来说，即使是最好的系统的输出也是乱码、不合语法的，并且难以解释。中文到英文的系统需要特别改进，尽管这对语言很重要，而英文到中文的翻译，同样重要的个人之间的沟通，很少被研究。该项目开发了在语义而不是表面水平上自动学习中文和英文之间对应关系的方法，使机器翻译能够从文本语义分析和自然语言生成的最新工作中受益。这项工作的一部分是确定什么类型的源语言句子的语义分析可以最好地通知翻译系统，重点是分析丢弃的参数，共指链接和子句之间的话语关系。这些语言现象在汉译英时通常必须更加明确。工作的第二部分将自然语言生成集成到统计机器翻译中，利用生成技术来确定句子边界，成分排序以及翻译系统容易出错的功能词的产生。第三部分开发并比较了基于语义表示的机器翻译模型的训练和解码算法。所有这些研究都利用新开发的语言资源进行汉语和英语的语义分析。改进的机器翻译技术的最终好处是更容易获得信息，更容易进行个人之间的交流。这反过来又导致贸易机会的增加，以及不同文化之间的更好理解。本项目的汉英和英汉翻译系统的开发是为了期望将来这些方法能应用于其他语言对。