arXiv:2007.14987 [cs.RO]

Presentation and Analysis of a Multimodal Dataset for Grounded Language Learning

Patrick Jenkins, Rishabh Sachdeva, Gaoussou Youssouf Kebe, Padraig Higgins, Kasra Darvish, Edward Raff, Don Engel, John Winder, Francis Ferraro, and Cynthia Matuszek

July 31, 2020

Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is a major 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 input. 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.

BibTeX OWL Tweet Scholar

Type: Article

Downloads: 407 downloads