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Most large language models are trained on linguistic input alone, yet humansappear to ground their understanding of words in sensorimotor experience. Anatural solution is to augment LM representations with human judgments of aword's sensorimotor associations (e.g., the Lancaster Sensorimotor Norms), butthis raises another challenge: most words are ambiguous, and judgments of wordsin isolation fail to account for this multiplicity of meaning (e.g., "woodentable" vs. "data table"). We attempted to address this problem by building anew lexical resource of contextualized sensorimotor judgments for 112 Englishwords, each rated in four different contexts (448 sentences total). We showthat these ratings encode overlapping but distinct information from theLancaster Sensorimotor Norms, and that they also predict other measures ofinterest (e.g., relatedness), above and beyond measures derived from BERT.Beyond shedding light on theoretical questions, we suggest that these ratingscould be of use as a "challenge set" for researchers building grounded languagemodels.

Contextualized Sensorimotor Norms: multi-dimensional measures of sensorimotor strength for ambiguous English words, in context