We take up the challenge of learning a grounded model of language when our agent has a body of machine learning algorithms and no prior knowledge of either the physical domain or language, in the sense of "least commitment". Based on a 2D video and co-occurring raw text, we demonstrate how this cognitively inspired model segments the world to obtain a meaning space, and combines words into hierarchical patterns for a linguistic pattern space. By associating these two spaces under temporal co-occurrence constraints, we demonstrate the acquisition of term-meaning pairs for names, actions and relations. We next map physical arguments for actions and relations to syntactical constructions resembling a cognitive grammar framework. Thus the system is able to bootstrap a rudimentary lexicon and syntax. While experiments are primarily in English, we present partial results for Hindi obtained without any change in the methods, to indicate its potential application to other languages.
['Sushobhan Nayak', 'Amitabha Mukerjee']
Grounded Language Acquisition: A Minimal Commitment Approach