Natural Language Acquisition for Machines - Reinforcement Learning of Minimalist Grammars

• Applicant: Professor Dr.-Ing. Matthias Wolff
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing ; General and Comparative Linguistics, Experimental Linguistics, Typology, Non-European Languages
• Term: from 2020 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 432615119

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.

• DFG Programme: Research Grants