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---
license: apache-2.0
language:
- en
tags:
- Natural Language Processing
- Generalize Quantifier
- Quantifier Reasoning
size_categories:
- n<1K
---
### Introduction
Generalized quantifiers (e.g., few, most) are used to indicate the proportions predicates are satisfied. QuRe is quantifier reasoning dataset from [Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models](https://arxiv.org/pdf/2311.04659). It includes real-world sentences from Wikipedia and human annotations of generalized quantifiers from English speakers.
### Sample
```
{
"orig_sentence": "In order for a steel to be considered stainless it must have a Chromium content of at least 10.5%.",
"percentage": "10.50%",
"percentage_index": 0,
"math_expr": ">=0.105",
"quant_sent": "In order for a steel to be considered stainless it must have some Chromium content.",
"quantifier": "some",
"quantifier_position": 12,
"specificity": "unable",
"wiki_entity": "List of blade materials",
"topics": "metallurgy; steel; composition"
}
```
* orig_sentence: the original sentence appeared in Wikipedia.
* percentage: the percentage mentioned in the orig_sentence.
* percentage_index: the index of the mentioned percentage in the orig_sentence.
* math_expr: the percentage expression generated.
* quant_sent: the annotated quantified sentence.
* quantifier_position: the position of quantifier mentioned.
* specificity: the difficulty of deciphering the percentage scope of the quantifier from the sentence excluding the quantifier.
* wiki_entity: the wikipedia entity that includes <i>orig_sentence</i> in the wikipage content.
* topics: sentence topics.
### Load Dataset
```python
from datasets import load_dataset
ds = load_dataset("billli/QuRe")
```
### Reference
```
@inproceedings{li-etal-2023-pragmatic,
title = "Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models",
author = "Li, Yiyuan and
Menon, Rakesh and
Ghosh, Sayan and
Srivastava, Shashank",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.38",
pages = "573--591",
}
``` |