Multilingual Statistical Machine Translation

多语言统计机器翻译

• 批准号: 262313-2013
• 负责人: Sarkar, Anoop
• 金额: $ 2.19万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635049
• 关键词: Multilingual; Statistical; Machine; Translation

This proposal is about machine translation from one natural language to another, such as translation from English to French. As a bilingual and multicultural nation, translation into multiple languages is socially important to Canada. Translation is also strategically important to Canada in order to facilitate our interactions with the world. There have been tremendous advances in the field of machine translation over the last two decades with rule-based approaches being replaced by statistical machine translation. Statistical machine translation solutions are now being offered by major companies like Google, SDL and IBM. However all contemporary translation systems rely on large amounts of data between specific language pairs such as French-English, ignoring many low-resource languages, e.g. Inuktitut, Haitian Kreyol, Malayalam, and Finnish (languages that are spoken by millions of people). This proposal aims to use linguistic insights about language families and use this information to develop multilingual statistical machine translation models and algorithms. We aim to transfer information from source-target language pairs with a lot of resources, such as French-English to low-resource language pairs, such as English-Inuktitut. This proposal addresses a basic unresolved issue in statistical machine translation research: learning a translation model between any pair of languages regardless of resource limitations.

该提案是关于从一种自然语言到另一种自然语言的机器翻译，例如从英语到法语的翻译。作为一个双语和多元文化的国家，翻译成多种语言对加拿大的社会意义重大。翻译对加拿大也具有重要的战略意义，以促进我们与世界的互动。在过去的二十年里，机器翻译领域取得了巨大的进步，基于规则的方法被统计机器翻译所取代。统计机器翻译解决方案现在由Google、SDL和IBM等大公司提供。然而，所有当代翻译系统都依赖于特定语言对（如法语-英语）之间的大量数据，而忽略了许多低资源语言，如因纽特语、海地语、马拉雅拉姆语和芬兰语（数百万人使用的语言）。该提案旨在利用对语言家族的语言学见解，并利用这些信息开发多语言统计机器翻译模型和算法。我们的目标是将信息从具有大量资源的源-目标语言对（如法语-英语）转移到低资源语言对（如英语-因纽特语）。该建议解决了统计机器翻译研究中一个基本的未解决问题：学习任何语言对之间的翻译模型，而不管资源限制。