Affordances in Grounded Language Learning

Stephen McGregor, KyungTae Lim


Abstract
We present a novel methodology involving mappings between different modes of semantic representation. We propose distributional semantic models as a mechanism for representing the kind of world knowledge inherent in the system of abstract symbols characteristic of a sophisticated community of language users. Then, motivated by insight from ecological psychology, we describe a model approximating affordances, by which we mean a language learner’s direct perception of opportunities for action in an environment. We present a preliminary experiment involving mapping between these two representational modalities, and propose that our methodology can become the basis for a cognitively inspired model of grounded language learning.
Anthology ID:
W18-2806
Volume:
Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing
Month:
July
Year:
2018
Address:
Melbourne
Venues:
ACL | CogACLL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
41–46
Language:
URL:
https://aclanthology.org/W18-2806
DOI:
10.18653/v1/W18-2806
Bibkey:
Cite (ACL):
Stephen McGregor and KyungTae Lim. 2018. Affordances in Grounded Language Learning. In Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing, pages 41–46, Melbourne. Association for Computational Linguistics.
Cite (Informal):
Affordances in Grounded Language Learning (McGregor & Lim, 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-2806.pdf