RI: Small: Statistical Machine Translation Through a Tree Adjoining Grammar with Flexible Parsing Operations

RI：Small：通过具有灵活解析操作的树邻接语法进行统计机器翻译

• 批准号: 1161814
• 负责人: Michael Collins
• 金额: $ 40.1万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-01-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1161814&HistoricalAwards=false
• 关键词: RI; Small; Statistical; Machine; Translation

Our research involves the development of a syntactic approach forstatistical machine translation that extends a tree adjoining grammar(TAG) formalism to the translation problem, and frames translationdirectly as a parsing problem. The model imposes no constraints onentries in the phrasal lexicon, thereby retaining the flexible lexicalentries of phrase-based translation systems; it allows straightforwardincorporation of a syntactic language model. The operations used tocombine tree fragments into a complete parse tree are generalizationsof standard parsing operations found in TAG; specifically, they aremodified to be highly flexible, potentially allowing any possiblepermutation (reordering) of the initial fragments. This allows themodel a great deal of freedom in capturing differences in word orderbetween source and target languages.The use of flexible parsing operations raises a couple of challengesthat are a major focus of our research. First, efficient decodingalgorithms are required for the models. Second, flexible parsingoperations allow the model to capture complex reordering phenomena,but in addition introduce many spurious possibilities. We areinvestigating the use of learned, probabilistic constraints based oninformation in the source-language sentence, or in a parse tree forthe source-language sentence, thereby incorporating syntacticinformation from the source language.The end goal of the project is to develop new models for translationthat improve the fluency or grammaticality of translations, improvethe degree to which semantic information (e.g., predicate-argumentstructure) is preserved in translation, and improve the treatment ofdiffering word orders between source and target languages.

我们的研究涉及到统计机器翻译的句法方法的发展，扩展了树邻接语法（TAG）的形式主义的翻译问题，并直接作为一个分析问题的框架解释。该模型对短语词典中的词条没有任何约束，从而保留了基于短语的翻译系统的灵活词条;它允许直接并入句法语言模型。用于将树片段组合成完整解析树的操作是TAG中标准解析操作的概括;具体来说，它们被修改为高度灵活，可能允许对初始片段进行任何可能的置换（重新排序）。这使得模型在捕捉源语言和目标语言之间的词序差异方面有很大的自由度。灵活的解析操作的使用提出了一些挑战，这是我们研究的主要焦点。首先，高效的解码算法是必需的模型。其次，灵活的解析操作允许模型捕捉复杂的重新排序现象，但也引入了许多虚假的可能性。我们正在研究基于源语言句子或源语言句子的解析树中的信息的学习的概率约束的使用，从而结合来自源语言的句法信息。该项目的最终目标是开发新的解释模型，以提高翻译的流畅性或语法性，提高语义信息（例如，谓词-论元结构）在翻译中得以保留，并改善了对源语言和目标语言之间不同语序的处理。