New Models for Statistical Machine Translation

机器翻译（MT）的统计方法是近年来自然语言处理领域大量研究的主题。一个开创性的工作在该领域是在IBM的研究人员在20世纪90年代初，许多统计MT内的原始概念在本文中介绍，和最近的工作在统计MT建立在这些ideas.This项目涉及的研究，调查IBM模型的替代方法。 特别是，我们正在研究使用全局特征向量表示的翻译，沿着歧视性的训练方法。 该项目的第一个目标是开发一个新的统计机器翻译框架。 第二个目标是将该方法用作机器翻译系统新功能的测试平台。 我们的目标是使用新的方法，使一个系统的研究问题，如词义消歧和使用的句法知识在统计机器翻译系统。提高机器翻译系统的质量可能会产生重大影响的社会。 最近，万维网或数字图书馆（英语或其他）的用户面临着大量的外语文本。 人工翻译本文本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访问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MT系统的广泛功能的研究。

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.