Abstract

Word learning models are typically evaluated as the problem of observing words together with sets of atomic objects and learn-ing an alignment between them. We use ADAM, a Python software platform for modeling grounded language acquisition, to evaluate a particular word learning model, Pursuit (Stevens, Gleitman, Trueswell, & Yang, 2017),under more realistic learning conditions (see e.g. Gleitman and Trueswell (2020) for review). In particular, we manipulate the degree of referential ambiguity and the salience of attentional cues available to the learner, and we present extensions to Pursuit which address the challenges of non-atomic meanings and exploiting attentional cues.

Date

2021

Authors

Ryan Gabbard, Jacob A Lichtefeld, Deniz Beser, Joe Cecil, Mitch Marcus, Sarah RB Payne, Charles Yang, Marjorie Freedman

Journal

Proceedings of the Annual Meeting of the Cognitive Science Society

Volume

43

Issue

43