Computational Models of Tutor Feedback in Language Acquisition

TL;DR

This paper explores computational models of tutor feedback in language acquisition, comparing existing paradigms and proposing a new mixed approach with novel algorithms to enhance understanding of social feedback's role.

Contribution

It introduces a new mixed paradigm combining interactive and cross-situational learning, along with algorithms tailored for mixed feedback experiments.

Findings

New mixed paradigm combining social and no feedback

Development of algorithms for mixed feedback scenarios

Performance analysis of algorithms against traditional methods

Abstract

This paper investigates the role of tutor feedback in language learning using computational models. We compare two dominant paradigms in language learning: interactive learning and cross-situational learning - which differ primarily in the role of social feedback such as gaze or pointing. We analyze the relationship between these two paradigms and propose a new mixed paradigm that combines the two paradigms and allows to test algorithms in experiments that combine no feedback and social feedback. To deal with mixed feedback experiments, we develop new algorithms and show how they perform with respect to traditional knn and prototype approaches.