Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Languages:
English
Size:
1K - 10K
Tags:
annotations_creators: | |
- expert-generated | |
language: | |
- en | |
language_creators: | |
- crowdsourced | |
license: [] | |
multilinguality: | |
- monolingual | |
pretty_name: 'TRIP: Tiered Reasoning for Intuitive Physics' | |
size_categories: | |
- 1K<n<10K | |
source_datasets: | |
- original | |
tags: [] | |
task_categories: | |
- text-classification | |
task_ids: | |
- natural-language-inference | |
# [TRIP - Tiered Reasoning for Intuitive Physics](https://aclanthology.org/2021.findings-emnlp.422/) | |
Official dataset for [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://aclanthology.org/2021.findings-emnlp.422/). Shane Storks, Qiaozi Gao, Yichi Zhang, Joyce Chai. EMNLP Findings, 2021. | |
For our official model and experiment code, please check [GitHub](https://github.com/sled-group/Verifiable-Coherent-NLU). | |
## Overview | |
![image](trip_sample.png) | |
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 | |
```python | |
from datasets import load_dataset | |
dataset = load_dataset("sled-umich/TRIP") | |
``` | |
* [HuggingFace-Dataset](https://huggingface.co/datasets/sled-umich/TRIP) | |
* [GitHub](https://github.com/sled-group/Verifiable-Coherent-NLU) | |
## Cite | |
```bibtex | |
@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}, | |
} | |
``` | |