Open AccessMeasuring Perceptual and Linguistic Complexity in Multilingual Grounded Language Data
Author(s) -
Nisha Pillai,
Cynthia Matuszek,
Francis Ferraro
Publication year - 2021
Publication title -
proceedings of the ... international florida artificial intelligence research society conference
Language(s) - English
Resource type - Journals
eISSN - 2334-0762
pISSN - 2334-0754
DOI - 10.32473/flairs.v34i1.128450
Subject(s) - computer science , perception , natural language processing , artificial intelligence , representation (politics) , language acquisition , linguistics , psychology , philosophy , neuroscience , politics , political science , law
The success of grounded language acquisition using perceptual data (e.g., in robotics) is affected by the complexity of both the perceptual concepts being learned and the language describing those concepts. We present methods for analyzing this complexity, using both visual features and entropy-based evaluation of sentences. Our work illuminates core, quantifiable statistical differences in how language is used to describe different traits of objects, and the visual representation of those objects. The methods we use provide an additional analytical tool for research in perceptual language learning.