Reinforcement Learning of Minimalist Numeral Grammars
| Autor: | Markus Huber, Ronald Römer, Werner Meyer, Peter beim Graben, Matthias Wolff |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Parsing Computer Science - Computation and Language Minimalist grammar Mental lexicon Computer science business.industry Computer Science - Artificial Intelligence computer.software_genre Lexicon Semantics Linguistic competence 030507 speech-language pathology & audiology 03 medical and health sciences Artificial Intelligence (cs.AI) Rule-based machine translation Artificial intelligence 0305 other medical science business Computation and Language (cs.CL) computer Generative grammar Natural language processing |
| Popis: | Speech-controlled user interfaces facilitate the operation of devices and household functions to laymen. State-of-the-art language technology scans the acoustically analyzed speech signal for relevant keywords that are subsequently inserted into semantic slots to interpret the user's intent. In order to develop proper cognitive information and communication technologies, simple slot-filling should be replaced by utterance meaning transducers (UMT) that are based on semantic parsers and a \emph{mental lexicon}, comprising syntactic, phonetic and semantic features of the language under consideration. This lexicon must be acquired by a cognitive agent during interaction with its users. We outline a reinforcement learning algorithm for the acquisition of the syntactic morphology and arithmetic semantics of English numerals, based on minimalist grammar (MG), a recent computational implementation of generative linguistics. Number words are presented to the agent by a teacher in form of utterance meaning pairs (UMP) where the meanings are encoded as arithmetic terms from a suitable term algebra. Since MG encodes universal linguistic competence through inference rules, thereby separating innate linguistic knowledge from the contingently acquired lexicon, our approach unifies generative grammar and reinforcement learning, hence potentially resolving the still pending Chomsky-Skinner controversy. 13 pages, 1 figure |
| Databáze: | OpenAIRE |
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