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

我们的主要研究目标是开发一种原型语音翻译通信器，它将为少数民族语言提供跨语言鸿沟的信息服务，以便允许远程语言不同的用户直接与互联网内容和数据库进行交流，更重要的是与使用不同语言的其他人进行交流。后者将使信息、教育和卫生服务能够到达偏远的少数民族语言社区。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更广泛的语言现象方面取得重大进展。传统的基于迁移规则的机器翻译需要长达一个世纪的时间来构建和完善一个新的语言对。统计和基于示例的机器翻译用大量的双语训练数据取代了人类的编码工作，这些数据对于大多数少数民族语言来说几乎是不可获得的。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，唯一在商业上合理的MT应用程序涉及主要的欧洲语言，日语，中文，韩语，阿拉伯语，也许还有一些相对流行的语言。目前，绝大多数人类语言都被扔进了众所周知的MT垃圾堆。我们提出了基于扩展和新的机器学习方法的新的机器翻译方法。第一种方法由统计MTmethods组成，该方法从数量级较小的训练数据中学习，并且可以通过使用联合源-通道建模方法与指数（最大熵）模型相结合，更有效地合并先前的语言信息（包括字典、词类和已知的语言规则类或约束）。第二种方法是一种从本地举报人获取高质量MTtransfer规则的新方法，它减少了对人类专家的依赖，缩短了开发时间。基于可控双语语料库和交互工具，通过一种新的局部约束的种子版本空间方法对语义条件迁移规则进行了推广，从而从本地告密者那里获取信息。第三种方法是建立跨多个语言族的通用电话模型，用于语音识别，并使识别器适应新语言，使用最少的新语言训练数据。所有这些方法都基于新的和现有的机器学习算法，这些算法将先验知识与有限数量的新数据相结合，以便快速收敛于工作的机器翻译、语音识别和合成系统。主要的社会影响将是对全球信息民主化作出重大贡献，这一进程需要消除目前的语言障碍，特别是低密度或经济上处于不利地位的语言。此外，新的语言学和声学知识与现有的教程软件相结合，将直接使濒危语言的保护和教学成为可能。如果成功，Avenue（可适应语音支持的自然翻译器，用于普遍授权）将成为机器翻译系统的原型，该系统将使全球范围内的多语言信息访问成为可能。

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.