RI: Large:Collaborative Research: Richer Representations for Machine Translation

RI：大型：协作研究：更丰富的机器翻译表示

• 批准号: 0910611
• 负责人: Daniel Gildea
• 金额: $ 54万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0910611&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.

人类语言机器翻译的研究最近取得了实质性进展，从在线双语文本自动收集的表面模式对于某些语言对来说效果非常好。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、不符合语法且难以解释的。尽管这种语言对很重要，但汉译英系统需要特别改进，而对于个人之间的交流同样重要的英译汉翻译却很少被研究。该项目开发了在语义而非表面层面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。这项工作的一部分确定了哪些类型的源语言句子语义分析可以最好地为翻译系统提供信息，重点分析删除的论点、共指链接和子句之间的话语关系。在汉译英时，这些语言现象通常必须更加明确。该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生成。第三部分开发并比较了用于训练和解码基于语义表示定义的机器翻译模型的算法。所有这些研究都利用新开发的语言资源进行中文和英语的语义分析。改进的机器翻译技术的最终好处是更容易获取信息和人与人之间的交流。这反过来又带来了贸易机会的增加以及文化之间更好的理解。该项目开发了汉英和英汉系统，期望这些方法将来能够应用于其他语言对。