Contextual Interpretation: An Adaptive Framework for Situated Reference Resolution

Abstract

This Data Science paper introduces an innovative model for interpreting referring expressions within grounded communication tasks. Our approach unifies speaker and listener neural networks through a recursive pragmatic reasoning framework, demonstrating significant improvements in interpretative accuracy, particularly for challenging distinctions. The system leverages an explicit feedback mechanism by combining the predictive perspectives of both speaker and listener components, which iteratively refines the interpretation process. This adaptive methodology, evaluated on a newly collected corpus, establishes a robust framework for nuanced contextual understanding, showcasing enhanced performance through synergistic model interaction.