TRIP - Tiered Reasoning for Intuitive Physics

Official dataset for Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding. Shane Storks, Qiaozi Gao, Yichi Zhang, Joyce Chai. EMNLP Findings, 2021.

For our official model and experiment code, please check GitHub.

Overview

We introduce Tiered Reasoning for Intuitive Physics (TRIP), a novel commonsense reasoning dataset with dense annotations that enable multi-tiered evaluation of machines’ reasoning process.

It includes dense annotations for each story capturing multiple tiers of reasoning beyond the end task. From these annotations, we propose a tiered evaluation, where given a pair of highly similar stories (differing only by one sentence which makes one of the stories implausible), systems must jointly identify (1) the plausible story, (2) a pair of conflicting sentences in the implausible story, and (3) the underlying physical states in those sentences causing the conflict. The goal of TRIP is to enable a systematic evaluation of machine coherence toward the end task prediction of plausibility. In particular, we evaluate whether a high-level plausibility prediction can be verified based on lower-level understanding, for example, physical state changes that would support the prediction.

Download

from datasets import load_dataset
dataset = load_dataset("sled-umich/TRIP")

• HuggingFace-Dataset
• GitHub

Cite

@misc{storks2021tiered,
      title={Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding}, 
      author={Shane Storks and Qiaozi Gao and Yichi Zhang and Joyce Chai},
      year={2021},
      booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021},
      location={Punta Cana, Dominican Republic},
      publisher={Association for Computational Linguistics},
}