Distrubutional Semantics Still Can't Account for Affordances
Can we know a word by the company it keeps? Aspects of meaning that concern physical interactions might be particularly difficult to learn from language alone. Glenberg & Robertson (2000) found that although human comprehenders were sensitive to the distinction between afforded and nonafforded actions, distributional semantic models were not. We tested whether technological advances have made distributional models more sensitive to affordances by replicating their experiment with modern Neural Language Models (NLMs). We found that only one NLM (GPT-3) was sensitive to the affordedness of actions. Moreover, GPT-3 accounted for only one third of the effect of affordedness on human sensibility judgments. These results imply that people use processes that go beyond distributional statistics to understand linguistic expressions, and that NLP systems may need to be augmented with such capabilities.