Datasets:

tum-nlp
/

cannot-dataset

	---
	language:
	- en
	pretty_name: CANNOT
	license: cc-by-sa-4.0
	size_categories:
	- 10K<n<100K
	---

	<p align="center"><img width="500" src="https://github.com/dmlls/cannot-dataset/assets/22967053/a380dfdf-3514-4771-90c4-636698d5043d" alt="CANNOT dataset"></p>
	<p align="center" display="inline-block">
	<a href="https://github.com/dmlls/cannot-dataset/">
	<img src="https://img.shields.io/badge/version-1.1-green">
	</a>
	</p>
	<h1 align="center">Compilation of ANnotated, Negation-Oriented Text-pairs</h1>

	---

	# Dataset Card for CANNOT

	## Dataset Description

	- Homepage: https://github.com/dmlls/cannot-dataset
	- Repository: https://github.com/dmlls/cannot-dataset
	- Paper: [This is not correct! Negation-aware Evaluation of Language Generation Systems](https://arxiv.org/abs/2307.13989)

	### Dataset Summary


	CANNOT is a dataset that focuses on negated textual pairs. It currently
	contains 77,376 samples, of which roughly of them are negated pairs of
	sentences, and the other half are not (they are paraphrased versions of each
	other).

	The most frequent negation that appears in the dataset is verbal negation (e.g.,
	will → won't), although it also contains pairs with antonyms (cold → hot).

	### Languages
	CANNOT includes exclusively texts in English.

	## Dataset Structure

	The dataset is given as a
	[`.tsv`](https://en.wikipedia.org/wiki/Tab-separated_values) file with the
	following structure:

	\| premise \| hypothesis \| label \|
	\|:------------\|:---------------------------------------------------\|:-----:\|
	\| A sentence. \| An equivalent, non-negated sentence (paraphrased). \| 0 \|
	\| A sentence. \| The sentence negated. \| 1 \|


	The dataset can be easily loaded into a Pandas DataFrame by running:

	```Python
	import pandas as pd

	dataset = pd.read_csv('negation_dataset_v1.0.tsv', sep='\t')

	```

	## Dataset Creation

	The dataset has been created by cleaning up and merging the following datasets:

	1. _Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal
	Negation_ (see
	[`datasets/nan-nli`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/nan-nli)).

	2. _GLUE Diagnostic Dataset_ (see
	[`datasets/glue-diagnostic`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/glue-diagnostic)).

	3. _Automated Fact-Checking of Claims from Wikipedia_ (see
	[`datasets/wikifactcheck-english`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/wikifactcheck-english)).

	4. _From Group to Individual Labels Using Deep Features_ (see
	[`datasets/sentiment-labelled-sentences`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/sentiment-labelled-sentences)).
	In this case, the negated sentences were obtained by using the Python module
	[`negate`](https://github.com/dmlls/negate).

	5. _It Is Not Easy To Detect Paraphrases: Analysing Semantic Similarity With
	Antonyms and Negation Using the New SemAntoNeg Benchmark_ (see
	[`datasets/antonym-substitution`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/antonym-substitution)).

	<br>

	Once processed, the number of remaining samples in each of the datasets above are:

	\| Dataset \| Samples \|
	\|:--------------------------------------------------------------------------\|-----------:\|
	\| Not another Negation Benchmark \| 118 \|
	\| GLUE Diagnostic Dataset \| 154 \|
	\| Automated Fact-Checking of Claims from Wikipedia \| 14,970 \|
	\| From Group to Individual Labels Using Deep Features \| 2,110 \|
	\| It Is Not Easy To Detect Paraphrases \| 8,597 \|
	\| <div align="right"><b>Total</b></div> \| 25,949 \|

	<br>

	Additionally, for each of the negated samples, another pair of non-negated
	sentences has been added by paraphrasing them with the pre-trained model
	[`🤗tuner007/pegasus_paraphrase`](https://huggingface.co/tuner007/pegasus_paraphrase).

	Finally, the swapped version of each pair (premise ⇋ hypothesis) has also been
	included, and any duplicates have been removed.

	With this, the number of premises/hypothesis in the CANNOT dataset that appear
	in the original datasets are:

	\| <div align="left"><b>Dataset</b></div> \| <div align="center"><b>Sentences</b></div> \|
	\|:--------------------------------------------------------------------------\|----------------------:\|
	\| Not another Negation Benchmark \| 552     (0.36 %) \|
	\| GLUE Diagnostic Dataset \| 586     (0.38 %) \|
	\| Automated Fact-Checking of Claims from Wikipedia \| 89,728   (59.98 %) \|
	\| From Group to Individual Labels Using Deep Features \| 12,626     (8.16 %) \|
	\| It Is Not Easy To Detect Paraphrases \| 17,198   (11.11 %) \|
	\| <div align="right"><b>Total</b></div> \| 120,690   (77.99 %) \|

	The percentages above are in relation to the total number of premises and
	hypothesis in the CANNOT dataset. The remaining 22.01 % (34,062 sentences) are
	the novel premises/hypothesis added through paraphrase and rule-based negation.


	## Additional Information

	### Licensing Information

	The CANNOT dataset is released under [CC BY-SA
	4.0](https://creativecommons.org/licenses/by-sa/4.0/).

	<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">
	<img alt="Creative Commons License" width="100px" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png"/>
	</a>

	### Citation
	Please cite our [INLG 2023 paper](https://arxiv.org/abs/2307.13989), if you use our dataset.
	BibTeX:
	```bibtex
	@misc{anschütz2023correct,
	title={This is not correct! Negation-aware Evaluation of Language Generation Systems},
	author={Miriam Anschütz and Diego Miguel Lozano and Georg Groh},
	year={2023},
	eprint={2307.13989},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

	### Contributions

	Contributions to the dataset can be submitted through the [project
	repository](https://github.com/dmlls/cannot-dataset).