Update README.md

README.md

@@ -14,128 +14,51 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
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+# CleanComedy
 
+Humour generation is a challenging task in natural language processing due to limited resources and the quality of existing datasets. Available humour language resources often suffer from toxicity and duplication, limiting their effectiveness for training robust models. In this paper, we present CleanComedy, a specialised, partially annotated corpus, which includes jokes in English and Russian languages. The dataset is a filtered collection of existing sources, where toxic jokes and duplicates are removed with various algorithmic filters. The end quality of the dataset is validated with human assessment. We also present subjective human  humour score annotation for 1,000 Russian and 1,000 English jokes providing detailed, ethical and comprehensive dataset for humour detection and generation tasks.
 
-- **Curated by:** [More Information Needed]
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+
+- **Curated by:** Dmitry Vikhorev, Daria Galimzianova, Svetlana Gorovaia, Elizaveta Zhemchuzhina, Ivan P. Yamshchikov
+- **Language(s) (NLP):** English, Russian
+- **License:** CC-BY-4.0
 
 ### Dataset Sources [optional]
 
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-## Uses
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-<!-- Address questions around how the dataset is intended to be used. -->
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-### Direct Use
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-<!-- This section describes suitable use cases for the dataset. -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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-[More Information Needed]
+- **Repository:** (https://github.com/gorovuha/CleanComedy)
+- **Paper [optional]:** [CleanComedy: Creating Friendly Humor through Generative Techniques](https://arxiv.org/pdf/2412.09203)
 
 ## Dataset Structure
 
 <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
 
-[More Information Needed]
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-## Dataset Creation
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-### Curation Rationale
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-<!-- Motivation for the creation of this dataset. -->
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-[More Information Needed]
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-### Source Data
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-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
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-#### Data Collection and Processing
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-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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-[More Information Needed]
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-#### Who are the source data producers?
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-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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-### Annotations [optional]
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-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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-#### Annotation process
+### CleanComedy English 
+Ethical filtered jokes with 2-scale score 
+44,481 instances
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+### CleanComedy English Gold 
+Ethical filtered jokes with human humour 5-scale score
+1,000 instances
 
-[More Information Needed]
+### CleanComedy Russian 
+Ethical filtered jokes with 2-scale score 
+40,926 instances
 
-#### Who are the annotators?
 
-<!-- This section describes the people or systems who created the annotations. -->
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-[More Information Needed]
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-#### Personal and Sensitive Information
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-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+### CleanComedy Russian Gold 
+Ethical filtered jokes with human humour 5-scale score
+1,000 instances
 
 **BibTeX:**
 
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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-## More Information [optional]
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-## Dataset Card Authors [optional]
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-## Dataset Card Contact
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+@misc{vikhorev2024cleancomedycreatingfriendlyhumor,
+      title={CleanComedy: Creating Friendly Humor through Generative Techniques}, 
+      author={Dmitry Vikhorev and Daria Galimzianova and Svetlana Gorovaia and Elizaveta Zhemchuzhina and Ivan P. Yamshchikov},
+      year={2024},
+      eprint={2412.09203},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.09203}, 
+}