Using speakers' referential intentions to model early cross-situational word learning

Area of Science:

• Cognitive Science
• Computational Linguistics
• Developmental Psychology

Background:

• Word learning presents a "chicken and egg" problem for beginners, lacking both word meanings and speaker intentions.
• Existing models often rely on inferred utterance meanings or cross-situational associations, but not both in parallel.

Purpose of the Study:

• To develop a computational model that solves word meaning and speaker intention inference problems concurrently.
• To test the model's efficacy against existing word learning theories and behavioral data.

Main Methods:

• Developed a novel computational model for word learning.
• Utilized annotated corpus data for model testing and validation.
• Compared model performance against established computational models.

Main Results:

• The model demonstrated superior precision in inferring word-object concept pairings compared to other models.
• Probabilistic inferences about speaker intentions were key to the model's success.
• The model successfully explained phenomena like mutual exclusivity and one-trial learning.

Conclusions:

• Parallel inference of word meanings and speaker intentions offers a more robust approach to computational word learning.
• The model provides a unified explanation for diverse word learning behaviors observed in children.
• This framework advances our understanding of early language acquisition and cognitive development.