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Deep learning approaches to natural language processing have made greatstrides in recent years. While these models produce symbols that convey vastamounts of diverse knowledge, it is unclear how such symbols are grounded indata from the world. In this paper, we explore the development of a privatelanguage for visual data representation by training emergent language (EL)encoders/decoders in both i) a traditional referential game environment and ii)a contrastive learning environment utilizing a within-class matching trainingparadigm. An additional classification layer utilizing neural machinetranslation and random forest classification was used to transform symbolicrepresentations (sequences of integer symbols) to class labels. These methodswere applied in two experiments focusing on object recognition and actionrecognition. For object recognition, a set of sketches produced by humanparticipants from real imagery was used (Sketchy dataset) and for actionrecognition, 2D trajectories were generated from 3D motion capture systems(MOVI dataset). In order to interpret the symbols produced for data in eachexperiment, gradient-weighted class activation mapping (Grad-CAM) methods wereused to identify pixel regions indicating semantic features which contributeevidence towards symbols in learned languages. Additionally, a t-distributedstochastic neighbor embedding (t-SNE) method was used to investigate embeddingslearned by CNN feature extractors.

Grounded Language Acquisition From Object and Action Imagery