|
--- |
|
annotations_creators: |
|
- expert-generated |
|
language_creators: |
|
- crowdsourced |
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language: |
|
- en |
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license: |
|
- apache-2.0 |
|
multilinguality: |
|
- monolingual |
|
pretty_name: 'probability_words_nli' |
|
size_categories: |
|
- 1K<n<10K |
|
source_datasets: |
|
- original |
|
task_categories: |
|
- text-classification |
|
- multiple-choice |
|
- question-answering |
|
task_ids: |
|
- open-domain-qa |
|
- multiple-choice-qa |
|
- natural-language-inference |
|
tags: |
|
- wep |
|
- words of estimative probability |
|
- probability |
|
- logical reasoning |
|
- soft logic |
|
- nli |
|
- natural-language-inference |
|
- reasoning |
|
- logic |
|
--- |
|
|
|
# Dataset accompanying the "Probing neural language models for understanding of words of estimative probability" article |
|
|
|
This dataset tests the capabilities of language models to correctly capture the meaning of words denoting probabilities (WEP). We used probabilitic soft logic to combine probabilistic statements expressed with WEP (WEP-Reaosning) and we also used the UNLI dataset (https://nlp.jhu.edu/unli/) to directly check whether models can detect the WEP matching human-annotated probabilities. |
|
The dataset can be used as natural langauge inference data (context, premise, label) or multiple choice question answering (context,valid_hypothesis, invalid_hypothesis). |
|
|
|
```bib |
|
@article{sileo2022probing, |
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title={Probing neural language models for understanding of words of estimative probability}, |
|
author={Sileo, Damien and Moens, Marie-Francine}, |
|
journal={arXiv preprint arXiv:2211.03358}, |
|
year={2022} |
|
} |
|
``` |
