New Models for Statistical Machine Translation

机器翻译的统计方法是近年来自然语言处理领域大量研究的课题。20世纪90年代初，IBM的研究人员在该领域做了一项开创性的工作；本文介绍了统计机器翻译中的许多原始概念，并且统计机器翻译中最近的许多工作都建立在这些思想的基础上。该项目涉及研究IBM模型的替代方法。特别是，我们正在研究翻译的全局特征向量表示的使用，以及判别训练方法。该项目的第一个目标是为统计机器翻译开发一个新的框架。第二个目标是将该方法用作机器翻译系统中新功能的测试平台。我们的目标是使用新的方法对统计机器翻译系统中的词义消歧和句法知识的使用等问题进行系统的研究。机器翻译系统质量的提高可能会对社会产生重大影响。最近，万维网或数字图书馆的用户（说英语或其他）面临着大量用外语写的文本。人工翻译这篇文章的任何重要部分都是不可行的。机器翻译系统可能会对我们以电子形式获取自然语言信息的能力产生重大影响。此外，这项研究将促进广泛的研究人员对机器翻译系统的广泛特征的研究。

Statistical approaches to machine translation (MT) have been the subject of a great deal of recent research in natural language processing. A seminal piece of work in the field is that of researchers at IBM in the early 1990s; many of the original concepts within statistical MT were introduced within this paper, and much of the more recent work in statistical MT has built upon these ideas.This project involves research that investigates alternative approaches to the IBM models. In particular, we are investigating the use of global feature-vector representations of translations, along with discriminative training methods. A first aim of the project is to develop a new framework for statistical machine translation. A second aim is to use the approach as a testbed for new features in machine translation systems. Our goal is to use the new approach to make a systematic study of issues such as word-sense disambiguation and the use of syntactic knowledge in statistical MT systems.Improvements in the quality of machine translation systems could have a major impact on society. Recently, users of the world wide web or digital libraries (English speaking or otherwise) face a huge amount of text written in foreign languages. Human translation of any significant proportion of this text is not feasible. Machine translation systems could make a major impact on our ability to access natural language information in electronic form. In addition, this research will facilitate research on a wide range of features in MT systems, by a wide range of researchers.