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

近年来，人类语言的机器翻译研究取得了实质性进展，从在线双语文本中自动收集的表面模式对某些语言对的翻译效果非常好。然而，对于许多语言对，即使是最好的系统的输出也是乱码的、不合语法的、难以解释的。尽管这对语言很重要，但汉英翻译系统需要特别改进，而英汉翻译对个人之间的交流同样重要，但很少有人研究。该项目开发了在语义而非表面水平上自动学习中英文对应关系的方法，使机器翻译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种类型的源语言句子的语义分析可以最好地为翻译系统提供信息，重点是分析省略的论点，共同引用链接和子句之间的话语关系。在汉译英的过程中，这些语言现象通常必须更加明确。第二部分的工作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、成分排序和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用于训练和解码基于语义表示的机器翻译模型的算法。这些研究都是利用新开发的语言资源进行汉英语义分析。改进机器翻译技术的最终好处是更容易获取信息和更容易进行个人之间的交流。这反过来又带来了更多的贸易机会，以及文化之间更好的理解。本计划的中文对英文及英文对中文系统的发展，期望将来能应用于其他语言对。

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.