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We present a novel method for using agent experiences gathered through anembodied simulation to ground contextualized word vectors to objectrepresentations. We use similarity learning to make comparisons betweendifferent object types based on their properties when interacted with, and toextract common features pertaining to the objects' behavior. We then use anaffine transformation to calculate a projection matrix that transformscontextualized word vectors from different transformer-based language modelsinto this learned space, and evaluate whether new test instances of transformedtoken vectors identify the correct concept in the object embedding space. Ourresults expose properties of the embedding spaces of four different transformermodels and show that grounding object token vectors is usually more helpful togrounding verb and attribute token vectors than the reverse, which reflectsearlier conclusions in the analogical reasoning and psycholinguisticliterature.

Grounding and Distinguishing Conceptual Vocabulary Through Similarity Learning in Embodied Simulations