ITR/PE: AVENUE: Adaptable Voice Translation for Minority Languages

ITR/PE：AVENUE：针对少数民族语言的自适应语音翻译

• 批准号: 0121631
• 负责人: Jaime Carbonell
• 金额: $ 250万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-15 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0121631&HistoricalAwards=false
• 关键词: ITR; PE; AVENUE; Adaptable; Voice

Our primary research goal is to develop a prototype voice-enabledtranslating communicator which will deliver information services acrossthe linguistic divide for minority languages in order allow remotelinguistically-diverse users to communicate directly with Internetcontent and databases, and more importantly to communicate with othersspeaking a different language from their own. The latter will enableinformation, education, and, for example, health services, to reachremote minority-language communities. Achieving this goal requiresmajor advances in machine learning for translation and in cross-languagespeech-recognition adaptability to wider language phenomena.Traditional transfer-rule-based MT requires up to a person-centuryto build and perfect a new language pair. Statistical andExample-Based MT replaces human coding effort by vast amounts ofbilingual training data, which are virtually unobtainable for mostminority languages. Without a radical advance, leading to anover-an-order-of-magnitude improvement in development time, theonly commercially justifiable MT applications involve the majorEuropean languages, Japanese, Chinese, Korean, Arabic and perhaps acouple more relatively-popular languages. The vast majority of humanlanguages are currently relegated to the proverbial MT dust heap.We propose new MT approaches based on extended and new machinelearning methods. The first approach consists of statistical MTmethods that learn from orders of magnitude less training data,and that can more effectively incorporate prior linguistic information(including dictionaries, word classes, and known linguisticrule classes or constraints) by using the joint source-channelmodeling approach combined with exponential (maximum entropy) models.The second approach is a new method for acquiring high-quality MTtransfer rules from native informants which decreases dependence onhuman experts and reduces development time. Semantically-conditionedtransfer rules are generalized via a new locally-constrainedSeeded Version-Space method based on a controlled bilingual corpusand interactive tools to elicit information from native informants.The third method builds general phone models across multiple languagefamilies for speech recognition and adapts the recognizer to newlanguages with minimal new- language training data. All of thesemethods are based on new and existing machine learning algorithmsthat combine prior knowledge with limited amounts of new data inorder to converge quickly on working machine translation and speechrecognition and synthesis systems.The primary societal impact will be a significant contributionto the global democratization of informa- tion, a process thatrequires bridging current linguistic barriers, especially forlow-density or economically- disadvantaged languages. Additionally,preservation and teaching of endangered languages will be directlyenabled by the new linguistic and acoustic knowledge coupled withexisting tutorial software. If successful, Avenue (Adaptable Voice-EnabledNatural-translator for Universal Empowerment) will be the prototypeof an MT system that will empower world-wide access to multilingualinformation.

我们的主要研究目标是开发一个原型的语音翻译通信器，它将提供跨语言鸿沟的少数民族语言的信息服务，以允许远程语言多样化的用户直接与互联网内容和数据库进行通信，更重要的是与其他人交流说不同的语言从自己的。后者将使信息、教育和卫生服务等能够到达偏远的少数民族语言社区。要实现这一目标，需要在翻译机器学习和跨语言语音识别适应性方面取得重大进展，以适应更广泛的语言现象。传统的基于转换规则的机器翻译需要长达一个人世纪的时间来建立和完善一个新的语言对。基于统计和实例的机器翻译通过大量的双语训练数据来代替人类的编码工作，而这些数据对于大多数少数民族语言来说几乎是无法获得的。如果没有根本性的进步，导致开发时间的一个数量级的改进，唯一商业上合理的机器翻译应用程序涉及主要的欧洲语言，日语，汉语，韩语，阿拉伯语，也许还有几种相对流行的语言。 绝大多数人类语言目前都被归入众所周知的机器翻译垃圾堆。我们提出了新的机器翻译方法的基础上扩展和新的机器学习方法。第一种方法由统计MT方法组成，该方法从数量级较少的训练数据中学习，并且可以更有效地结合先前的语言信息（包括字典、词类、和已知的语言规则类或约束），通过使用联合源通道建模方法结合指数（最大熵）模型。第二种方法是一种新的方法，质量MT传输规则从本地的线人，减少对人类专家的依赖，并减少开发时间。第三种方法是基于受控双语语料库和交互式工具，通过一种新的局部约束的种子版本空间方法来推广语义条件转换规则，从而从母语信息者那里获取信息;第三种方法是建立跨多个语言家族的通用音素模型，用于语音识别，并以最少的新语言训练数据使识别器适应新语言。所有这些方法都基于新的和现有的机器学习算法，这些算法将联合收割机先验知识与有限的新数据结合起来，以便快速收敛到工作机器翻译和语音识别与合成系统上。其主要社会影响将是对全球信息民主化的重大贡献，这一过程需要弥合当前的语言障碍，特别是对于低密度或经济上处于劣势的语言。此外，濒危语言的保护和教学将直接通过新的语言学和声学知识与现有的教程软件相结合来实现。如果成功的话，Avenue（Adaptable Voice-EnabledNatural-translator for Universal Empowerment）将成为机器翻译系统的原型，它将使全世界能够获得多种语言的信息。