Overcoming Data Sparsity in Machine Translation

克服机器翻译中的数据稀疏性

• 批准号: RGPIN-2017-05875
• 负责人: Kondrak, Grzegorz
• 金额: $ 1.68万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679369
• 关键词: Overcoming; Data; Sparsity; Machine; Translation

Canada is a multicultural society. A large percentage of Canadian residents report a mother tongue that is distinct from either English or French. In addition, Canada is home to a rich variety of indigenous languages, some of which have also been granted official status. Everyone has the right to get all official federal government services, publications and documents in both English and French. Important information for new Canadians is often provided in multiple languages and scripts. Increasing the availability of texts in aboriginal languages increases their prestige, and thus helps preserve them.******As a consequence, there exists an acute need for accurate and rapid translations, not only between English and French, but also into other languages. Human translation is slow and expensive, and requires highly-skilled experts. Computer translation programs, known as machine translation, have the potential to fill the gap. Unfortunately, the current technology is far from perfect. The quality of translations involving smaller languages is often poor, and even between major languages, it is sometimes inadequate for technical applications.******Two of the reasons for the low quality of machine translation are the scarcity of bilingual texts for low-resourced languages, and the prevalence of infrequent words, such as certain verb inflections in French. The dominant statistical machine translation approach, which is used in web programs such as Google Translate, struggles to properly translate words that occur only rarely in bilingual texts.******The objective of this proposal is to improve the quality of machine translation by improving the handling of infrequent words. The principal research directions are the incorporation of the state-of-the-art morphological techniques into the translation process, the development of lexicon induction methods, and the translation of out-of-vocabulary words based on the cutting-edge algorithms for cognate identification, name transliteration, and decipherment.******In the current global economy, the enormous demand for fast and freely-available translations can only be satisfied by the machine translation programs. The solutions that I outline in my proposal will not only improve the quality of machine translation, but also influence the research on other aspects of natural language processing, thus accelerating the progress towards the goal of making computers understand human language.

加拿大是一个多元文化的社会。很大比例的加拿大居民报告说，他们的母语不同于英语或法语。此外，加拿大是各种土著语言的家园，其中一些语言也被授予官方地位。每个人都有权获得所有正式的联邦政府服务、出版物和文件的英文和法文版本。为新加拿大人提供的重要信息通常以多种语言和文字提供。增加土著语言文本的可用性可以提高他们的声望，从而有助于保护他们。因此，迫切需要准确和迅速的翻译，不仅是英文和法文之间的翻译，而且也需要翻译成其他语文。人工翻译速度慢，成本高，需要高技能的专家。被称为机器翻译的计算机翻译程序有可能填补这一差距。不幸的是，目前的技术还远远不够完美。涉及较小语种的翻译质量往往很差，即使是主要语种之间的翻译，有时也不足以满足技术应用的需要。机器翻译质量低的两个原因是缺乏资源匮乏的语言的双语文本，以及不常见的单词的流行，例如法语中的某些动词变化。在Google翻译等网络程序中使用的占主导地位的统计机器翻译方法，很难正确翻译双语文本中很少出现的单词。该提案的目的是通过改进对不常用词的处理来提高机器翻译的质量。主要研究方向是将最先进的形态学技术融入翻译过程，词汇归纳方法的发展，以及基于同源识别，名称音译和解密的尖端算法的词汇外词的翻译。在当前的全球经济中，对快速和免费翻译的巨大需求只能通过机器翻译程序来满足。我在提案中概述的解决方案不仅会提高机器翻译的质量，而且会影响自然语言处理其他方面的研究，从而加速实现计算机理解人类语言的目标。