Datasets:
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README.md
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Splits](#data-instances)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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## Dataset Description
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- **Leaderboard:** N/A
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- **Point of Contact:** [Severine Verlinden](mailto:severine.verlinden@outlook.be)
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### Dataset Summary
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CommonGen [Lin et al., 2020](https://arxiv.org/abs/1911.03705) is a dataset for the constrained text generation task of word inclusion. But the task does not allow to include context. Therefore, to complement CommonGen, we provide an extended test set where an additional context is provided for each set of target words. The task is therefore reformulated to both generate commonsensical text which include the given words, and also have the generated text adhere to the given context.
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### Supported Tasks and Leaderboards
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N/A
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### Languages
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C2Gen was created because the authors of the paper believed that the task formulation of CommonGen is too narrow, and that it needlessly incentivizes researchers
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to focus on methods that do not support context. Which is orthogonal to their belief that many application areas necessitates the consideration of surrounding context. Therefore, to complement CommonGen, they provide an extended test set where an additional context is provided for each set of target words.
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#### Initial Data Collection and Normalization
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The dataset was constructed with the help the crowd sourcing platform MechanicalTurk. Each remaining concept set manually received a textual context. To assure the quality of the data generation, only native English speakers with a recorded high acceptance were allowed to participate. Finally, all contexts were manually verified, and fixed in terms of typos and poor quality. Furthermore we want to raise awareness that C2GEN can contain personal data or offensive content. If you would encounter such a sample, please reach out to us.
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[Needs More Information]
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### Annotations
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#### Annotation process
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[Needs More Information]
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#### Who are the annotators?
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[Needs More Information]
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### Personal and Sensitive Information
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[Needs More Information]
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## Considerations for Using the Data
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### Social Impact of Dataset
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Further research is needed on this behalf.
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### Discussion of Biases
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[Needs More Information]
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### Other Known Limitations
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[Needs More Information]
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## Additional Information
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### Dataset Curators
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[Needs More Information]
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### Licensing Information
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license: cc-by-sa-4.0
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### Citation Information
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[Needs More Information]
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Splits](#data-instances)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Initial Data Collection and Normalization](#initial-cata-collection-and-normalization)
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- [Licensing Information](#licensing-information)
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## Dataset Description
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- **Repository:** [Non-Residual Prompting](https://github.com/FreddeFrallan/Non-Residual-Prompting)
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- **Paper:** [Fine-Grained Controllable Text Generation Using Non-Residual Prompting](https://aclanthology.org/2022.acl-long.471)
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- **Point of Contact:** [Fredrik Carlsson](mailto:Fredrik.Carlsson@ri.se)
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### Dataset Summary
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CommonGen [Lin et al., 2020](https://arxiv.org/abs/1911.03705) is a dataset for the constrained text generation task of word inclusion. But the task does not allow to include context. Therefore, to complement CommonGen, we provide an extended test set C2Gen [Carlsson et al., 2022](https://aclanthology.org/2022.acl-long.471) where an additional context is provided for each set of target words. The task is therefore reformulated to both generate commonsensical text which include the given words, and also have the generated text adhere to the given context.
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### Languages
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C2Gen was created because the authors of the paper believed that the task formulation of CommonGen is too narrow, and that it needlessly incentivizes researchers
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to focus on methods that do not support context. Which is orthogonal to their belief that many application areas necessitates the consideration of surrounding context. Therefore, to complement CommonGen, they provide an extended test set where an additional context is provided for each set of target words.
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### Initial Data Collection and Normalization
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The dataset was constructed with the help the crowd sourcing platform MechanicalTurk. Each remaining concept set manually received a textual context. To assure the quality of the data generation, only native English speakers with a recorded high acceptance were allowed to participate. Finally, all contexts were manually verified, and fixed in terms of typos and poor quality. Furthermore we want to raise awareness that C2GEN can contain personal data or offensive content. If you would encounter such a sample, please reach out to us.
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## Licensing Information
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license: cc-by-sa-4.0
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