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--- |
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language: |
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- en |
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pretty_name: CANNOT |
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--- |
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# Dataset Card for CANNOT |
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## Dataset Description |
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- **Homepage: https://github.com/dmlls/cannot-dataset** |
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- **Repository: https://github.com/dmlls/cannot-dataset** |
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- **Paper: tba** |
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### Dataset Summary |
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**CANNOT** is a dataset that focuses on negated textual pairs. It currently |
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contains **77,376 samples**, of which roughly of them are negated pairs of |
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sentences, and the other half are not (they are paraphrased versions of each |
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other). |
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The most frequent negation that appears in the dataset is verbal negation (e.g., |
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will → won't), although it also contains pairs with antonyms (cold → hot). |
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### Languages |
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CANNOT includes exclusively texts in **English**. |
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## Dataset Structure |
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The dataset is given as a |
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[`.tsv`](https://en.wikipedia.org/wiki/Tab-separated_values) file with the |
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following structure: |
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| premise | hypothesis | label | |
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|:------------|:---------------------------------------------------|:-----:| |
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| A sentence. | An equivalent, non-negated sentence (paraphrased). | 0 | |
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| A sentence. | The sentence negated. | 1 | |
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The dataset can be easily loaded into a Pandas DataFrame by running: |
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```Python |
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import pandas as pd |
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dataset = pd.read_csv('negation_dataset_v1.0.tsv', sep='\t') |
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``` |
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## Dataset Creation |
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The dataset has been created by cleaning up and merging the following datasets: |
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1. _Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal |
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Negation_ (see |
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[`datasets/nan-nli`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/nan-nli)). |
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2. _GLUE Diagnostic Dataset_ (see |
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[`datasets/glue-diagnostic`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/glue-diagnostic)). |
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3. _Automated Fact-Checking of Claims from Wikipedia_ (see |
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[`datasets/wikifactcheck-english`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/wikifactcheck-english)). |
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4. _From Group to Individual Labels Using Deep Features_ (see |
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[`datasets/sentiment-labelled-sentences`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/sentiment-labelled-sentences)). |
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In this case, the negated sentences were obtained by using the Python module |
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[`negate`](https://github.com/dmlls/negate). |
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Additionally, for each of the negated samples, another pair of non-negated |
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sentences has been added by paraphrasing them with the pre-trained model |
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[`🤗tuner007/pegasus_paraphrase`](https://huggingface.co/tuner007/pegasus_paraphrase). |
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Furthermore, the dataset from _It Is Not Easy To Detect Paraphrases: Analysing |
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Semantic Similarity With Antonyms and Negation Using the New SemAntoNeg |
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Benchmark_ (see |
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[`datasets/antonym-substitution`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/antonym-substitution)) |
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has also been included. This dataset already provides both the paraphrased and |
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negated version for each premise, so no further processing was needed. |
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Finally, the swapped version of each pair (premise ⇋ hypothesis) has also been |
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included, and any duplicates have been removed. |
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The contribution of each of these individual datasets to the final CANNOT |
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dataset is: |
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| Dataset | Samples | |
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|:--------------------------------------------------------------------------|-----------:| |
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| Not another Negation Benchmark | 118 | |
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| GLUE Diagnostic Dataset | 154 | |
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| Automated Fact-Checking of Claims from Wikipedia | 14,970 | |
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| From Group to Individual Labels Using Deep Features | 2,110 | |
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| It Is Not Easy To Detect Paraphrases | 8,597 | |
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| <p align="right"><b>Total</b></p> | **25,949** | |
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_Note_: The numbers above include only the original queries present in the |
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datasets. |
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## Additional Information |
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### Licensing Information |
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TODO |
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### Citation Information |
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tba |
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### Contributions |
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Contributions to the dataset can be submitted through the [project repository](https://github.com/dmlls/cannot-dataset). |
