Update README.md

README.md

@@ -20,12 +20,24 @@ 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,