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: 0910532
• 负责人: Nianwen Xue
• 金额: $ 55.98万
• 依托单位: Brandeis University
• 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 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0910532&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.

最近，人类语言的机器翻译研究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一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的、不符合语法的，并且很难解释。尽管这对语言很重要，但汉译英的系统需要特别改进，而对于个人之间的交流同样重要的英译汉则很少有人研究。该项目开发了在语义而不是表面层面上自动学习汉语和英语之间的对应关系的方法，使机器翻译能够受益于最近在文本语义分析和自然语言生成方面的工作。这项工作的一部分决定了哪些类型的源语句子的语义分析最能为翻译系统提供信息，重点是分析省略的论元、共指链接和小句之间的语篇关系。这些语言现象在从汉语到英语的翻译中通常必须更加明确。这项工作的第二部分将自然语言生成整合到统计机器翻译中，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虚词的产生。第三部分开发和比较了用于训练和解码基于语义表示的机器翻译模型的算法。所有这些研究都是利用新近发展起来的语言学资源来进行汉语和英语的语义分析。改进机器翻译技术的最终好处是更容易地获取信息，更容易在个人之间进行交流。这反过来又带来了更多的贸易机会，以及更好的文化间理解。该项目开发了汉译英和英译汉两个系统，希望这些方法将来也能应用到其他语言对中。