Humans' experience of the world is profoundly multimodal from the beginning, so why do existing state-of-the-art language models only use text as a modality to learn and represent semantic meaning? In this paper we review the literature on the role of embodiment and emotion in the interactive setting of spoken dialogue as necessary prerequisites for language learning for human children, including how words in child vocabularies are largely concrete, then shift to become more abstract as the children get older. We sketch a model of semantics that leverages current transformer-based models and a word-level grounded model, then explain the robot-dialogue system that will make use of our semantic model, the setting for the system to learn language, and existing benchmarks for evaluation.
@article{arxiv.2105.04633,
title = {Language Acquisition is Embodied, Interactive, Emotive: a Research Proposal},
author = {Casey Kennington},
journal= {arXiv preprint arXiv:2105.04633},
year = {2021}
}