Papers by Tom M. Mitchell

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Challenge:	Probabilistic worldbuilding model is a Bayesian model of semantic parsing and reasoning . large-scale language models are domain-general, despite training on text from virtually every domain .
Approach:	They propose a Bayesian probabilistic worldbuilding model that parses and abduces sentences . they use a dataset to test their method against heuristics and to generate a probability model .
Outcome:	The proposed model outperforms baselines on two out-of-domain question-answering datasets.