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说他们的母语既不是英语也不是法语。此外，加拿大拥有丰富多样的土著语言，其中一些还被授予官方地位。每个人都有权获得英语和法语两种语言的联邦政府官方服务、出版物和文件。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提高了他们的声望，从而有助于保护他们。******因此，不仅在英语和法语之间，而且在其他语言之间，都迫切需要准确和快速的翻译。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空白。不幸的是，目前的技术还远远不够完美。涉及较小语言的翻译质量往往很差，即使在主要语言之间，有时也不足以用于技术应用。******机器翻译质量不高的两个原因是资源匮乏的语言缺乏双语文本，以及不常见的单词普遍存在，例如法语中的某些动词屈折。在谷歌Translate等网络程序中使用的主流统计机器翻译方法难以正确翻译双语文本中很少出现的单词。******这个建议的目的是通过改进对不频繁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纳入翻译过程，发展词汇归纳方法，以及基于同源识别、名称音译和译码的前沿算法的词汇外词翻译。******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，还会影响自然语言处理其他方面的研究，从而加速实现让计算机理解人类语言的目标。