Tiered Reasoning for Intuitive Physics (TRIP) is a novel commonsense reasoning dataset with dense annotations that enable multi-tiered evaluation of machines’ reasoning process. TRIP serves as a benchmark for physical commonsense reasoning that provides traces of reasoning for an end task of plausibility prediction. The dataset consists of human-authored stories describing sequences of concrete physical actions. Given two stories composed of individually plausible sentences and only differing by one sentence (i.e., Sentence 5), the proposed task is to determine which story is more plausible. To understand stories like these and make such a prediction, one must have knowledge of verb causality and precondition, and rules of intuitive physics.

Description from: Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding