Presentation and Analysis of a Multimodal Dataset for Grounded Language Learning

TL;DR

This paper introduces the Grounded Language Dataset (GoLD), a multimodal collection of household object descriptions in speech and text, to advance grounded language learning in robotics, NLP, and HCI.

Contribution

The work presents a new multimodal dataset for grounded language learning and analyzes how different modalities influence language acquisition and interaction.

Findings

Different modalities impact language learning effectiveness.

Multimodal data reveals variations in human descriptions.

The dataset enables cross-modal analysis in grounded language research.

Abstract

Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is amajor area of research in robotics, NLP, and HCI. In practice the data used for learning consists almost entirely of textual descriptions, which tend to be cleaner, clearer, and more grammatical than actual human interactions. In this work, we present the Grounded Language Dataset (GoLD), a multimodal dataset of common household objects described by people using either spoken or written language. We analyze the differences and present an experiment showing how the different modalities affect language learning from human in-put. This will enable researchers studying the intersection of robotics, NLP, and HCI to better investigate how the multiple modalities of image, text, and speech interact, as well as show differences in the vernacular of these modalities impact results.