---
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",
}
```