Grammar Formalisms beyond Context-Free Grammars and their use for Machine Learning Tasks

这个项目的目的是开发用于概率分析和统计机器翻译的新模型和算法，使用诸如线性上下文无关重写系统(LCFRS)之类的形式，即上下文无关文法的扩展，将同步语法的各个方面与描述不连续的能力相结合。句法分析和机器翻译都面临着必须处理非本地依赖的挑战，换句话说，在句子中使用属于一起的不连续单元。解释这些非本地现象的一种方法是使用形式主义，这些形式主义可以描述的不仅仅是上下文无关的结构。在项目的第一部分中，这已经在解析中成功完成，我们使用LCFRS。我们计划在项目的第二部分继续这方面的研究。关于基于句法的统计机器翻译，我们在项目的第一部分已经表明，同步LCFR可以描述在平行语料库中遇到的有时复杂的对齐。因此，在项目的第二部分，我们的目标是完成(已经开始)的基于同步LCFRS的统计机器翻译系统的实现。

The aim of this project is to develop new models and algorithms forprobabilistic parsing and for statistical machine translation, usingformalisms such as Linear Context-Free Rewriting Systems (LCFRSs),i.e., extensions of Context-Free Grammars that combine aspects ofsynchronous grammars with the capacity to describe discontinuities.Both, parsing and machine translation, face the challenge that theyhave to deal with non-local dependencies, to put it differently, withdiscontinuous units in a sentence that belong together. One way toaccount for these non-local phenomena is to use formalisms that candescribe more than just context-free structures. In the first part ofthe project, this has already been successfully done in parsing, wherewe use LCFRS. We plan to continue this line of research in the secondpart of the project. Concerning syntax-based statistical machinetranslation, we have shown in the first part of the project thatsynchronous LCFRS can describe the sometimes complex alignments oneencounters in parallel corpora. In the second part of the project wetherefore aim at completing the (already started) implementation of astatistical machine translation system based on synchronous LCFRS.