CAREER: Semantics for Statistical Machine Translation

职业：统计机器翻译语义

• 批准号: 0546554
• 负责人: Daniel Gildea
• 金额: $ 50万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546554&HistoricalAwards=false
• 关键词: CAREER; Semantics; Statistical; Machine; Translation

The past few years have seen a revolution in machinetranslation, with the widespread adoption ofstatistical systems trained on large amounts ofparallel bilingual text. Recent evaluations have shownthat current statistically trained research technologysignificantly outperforms commercially available MTsystems such as those available on the web. But evenstate-of-the-art systems produce garbled translationsmore often than not. Further improvements in machinetranslation will require major changes in thearchitecture of statistical systems. Our research aimsto improve the quality of machine translation output byallowing statistical systems to handle deeper, semanticrepresentations.Our approach focuses on improving statistical machinetranslation by using a semantic representation at thelevel of predicate-argument structure. This workbuilds on the recent success in statistical approachesto shallow language understanding, and tree-basedalgorithms for machine translation using syntacticparses of the source and target sentences. Over thecourse of the project we aim to: first, develop robustsemantic parsing systems capable of generalizing to newdomains and apply them to large bilingual corpora,second, develop probabilistic models of translationthat use the resulting level of representation and canbe practically trained, and third, integrate languageunderstanding and translation to allow efficient searchfor the best overall translation of new sentences.

在过去的几年中，机械学的革命发生了一场革命，广泛采用的统计系统接受了大量的平行双语文本训练。 最近的评估已经显示了当前经过统计训练的研究技术，其表现非常高于市售的MTSystems，例如网络上可用的MTSYSTEMS。 但是，偶然的系统通常会产生乱码的翻译。 机械倾斜的进一步改进将需要对统计系统进行搜索的重大更改。 我们的研究目的是提高机器翻译输出的质量，从统计系统来处理更深层的统计系统。 这是关于统计方法浅语语言理解的最新成功的工作构建，以及使用源和目标句子的句法句法基于树的算法来进行机器翻译。 我们的目标是：首先，要开发能够概括为新界的强大语义解析系统，并将其应用于大型双语语料库中，其次，开发了翻译的概率模型，这是使用由此产生的表述水平的，并在实践中受过训练的培训，并在第三次进行了语言台阶和翻译，以有效地搜索最佳的整体翻译。