Modelling constructivist language acquisition through syntactico-semantic pattern finding

Area of Science:

• Psycholinguistics
• Cognitive Science
• Artificial Intelligence

Background:

• Child language acquisition is well-researched but lacks computational models.
• Existing AI communication models do not faithfully replicate human-like learning mechanisms.

Purpose of the Study:

• To introduce a mechanistic computational model for constructivist language acquisition.
• To operationalize language learning through syntactico-semantic pattern finding.

Main Methods:

• Developed a methodology for learning grammars from linguistic observations based on form-meaning similarities and differences.
• Created grammars consisting of variable-extent, abstract form-meaning mappings called constructions.
• Applied the methodology to the CLEVR benchmark dataset for proof of concept.

Main Results:

• Demonstrated online, incremental, and data-efficient learning of construction grammars.
• Showcased transparent and effective learning from utterance-meaning pairs.
• Validated the model's ability to learn item-based construction grammars.

Conclusions:

• The proposed model offers a faithful computational operationalization of child language acquisition.
• This approach facilitates both language comprehension and production in artificial intelligence.
• The methodology proves effective for learning construction grammars in a data-efficient manner.