CANNOT
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## 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).
<br>
## Languages
CANNOT includes exclusively texts in English.
<br>
## Dataset Structure
The dataset is given as a
.tsv 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')
`
<br>
## 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).
2. _GLUE Diagnostic Dataset_ (see
datasets/glue-diagnostic).
3. _Automated Fact-Checking of Claims from Wikipedia_ (see
datasets/wikifactcheck-english).
4. _From Group to Individual Labels Using Deep Features_ (see
datasets/sentiment-labelled-sentences).
In this case, the negated sentences were obtained by using the Python module
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).
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 |
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.
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.
<br>
## Additional Information
<br>
### Licensing Information
The CANNOT dataset is released under CC 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>
<br>
### Citation
Please cite our INLG 2023 paper, 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}
}
``
<br>
### Contributions
Contributions to the dataset can be submitted through the project
repository.