Natural Language Acquisition for Machines - Reinforcement Learning of Minimalist Grammars

语音操作的用户界面便于设备和家庭助理的控制。指令和反应可以通过语音控制进行交流，而不是使用键盘和显示器作为输入输出接口。特别是，智能家居开发和健康科学应用的智能用户界面在不久的将来变得越来越重要。为了用自然语言与用户交流，“认知代理”需要以“心理词典”的形式提供语言知识，该词典存储单词的发音和拼写、可能的句法组合及其含义。最先进的技术需要手工处理这些数据库的专家，因此在很大程度上排除了日常技术应用。该项目旨在在机器学习过程中以交互方式获取心理词汇。为了实现这一目标，该项目将应用计算语言学在语言技术方面的最新发现。乔姆斯基创立的生成语法的“极简程序”提供了一种复杂的自然语言语法理论，已成功地应用于语法、语义和音系的连接和正式实现。对于建议的项目来说，极简语法最重要的特点是它们通过积极的例子有效地易学。在语言习得中承认否定例子，可以避免不符合语法的概括。正面和负面证据的结合是“强化学习”的一个显著标志，追溯到斯金纳，强化学习成为人工智能研究和自适应行为控制的重要方法。

Voice-operated user interfaces facilitate the control of devices and home assistants. Instructions and responses can be communicated via voice control, instead of using keyboards and displays as input- output interfaces. In particular, intelligent user interfaces for smart home developments and applications in health science become increasingly important in the near future. In order to communicate with a user in natural language, a "cognitive Agent" requires linguistic knowledge in form of a "mental lexicon" that stores word pronunciations and spellings, possible syntactic combinations and their meanings. State-of-the-art technology requires experts who handcraft these data bases, hence considerably excluding technical every-day applications. The project aims at acquiring the mental lexicon interactively during machine learning. In order to achieve this, the project will apply recent findings from computational linguistics in language technology. The "minimalist program" for generative grammar, founded by Chomsky, provides a sophisticated theory of natural language grammar that has successfully been applied for conjoining and formally implementing syntax, semantic and phonology. The most important feature of minimalist grammar for the suggested project is their effective learnability by means of positive examples. Admitting negative examples for language acquisition as well, could avoid the utterance of ungrammatical generalizations. The combination of positive and negative evidence is a distinguished hallmark for “reinforcement learning” which, going back to Skinner, became an essential method for artificial intelligence research and adaptive control of behavior.