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--- |
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annotations_creators: |
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- expert-generated |
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language: |
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- en |
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language_creators: |
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- crowdsourced |
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license: [] |
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multilinguality: |
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- monolingual |
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pretty_name: 'TRIP: Tiered Reasoning for Intuitive Physics' |
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size_categories: |
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- 1K<n<10K |
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source_datasets: |
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- original |
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tags: [] |
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task_categories: |
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- text-classification |
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task_ids: |
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- natural-language-inference |
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--- |
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# [TRIP - Tiered Reasoning for Intuitive Physics](https://aclanthology.org/2021.findings-emnlp.422/) |
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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. |
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For our official model and experiment code, please check [GitHub](https://github.com/sled-group/Verifiable-Coherent-NLU). |
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## Overview |
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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. |
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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. |
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## Download |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("sled-umich/TRIP") |
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``` |
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* [HuggingFace-Dataset](https://huggingface.co/datasets/sled-umich/TRIP) |
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* [GitHub](https://github.com/sled-group/Verifiable-Coherent-NLU) |
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## Cite |
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```bibtex |
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@misc{storks2021tiered, |
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title={Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding}, |
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author={Shane Storks and Qiaozi Gao and Yichi Zhang and Joyce Chai}, |
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year={2021}, |
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booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021}, |
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location={Punta Cana, Dominican Republic}, |
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publisher={Association for Computational Linguistics}, |
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} |
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``` |
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