Probing Neural Language Models for Human Tacit Assumptions

• URL: http://arxiv.org/abs/2004.04877v2
• Date: Tue, 16 Jun 2020 15:55:51 GMT
• Title: Probing Neural Language Models for Human Tacit Assumptions
• Authors: Nathaniel Weir, Adam Poliak, Benjamin Van Durme
• Abstract summary: Humans carry stereotypic tacit assumptions (STAs) or propositional beliefs about generic concepts. We construct a diagnostic set of word prediction prompts to evaluate whether recent neural contextualized language models trained on large text corpora capture STAs.
• Score: 36.63841251126978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans carry stereotypic tacit assumptions (STAs) (Prince, 1978), or propositional beliefs about generic concepts. Such associations are crucial for understanding natural language. We construct a diagnostic set of word prediction prompts to evaluate whether recent neural contextualized language models trained on large text corpora capture STAs. Our prompts are based on human responses in a psychological study of conceptual associations. We find models to be profoundly effective at retrieving concepts given associated properties. Our results demonstrate empirical evidence that stereotypic conceptual representations are captured in neural models derived from semi-supervised linguistic exposure.

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