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@@ -44,7 +44,7 @@ This dataset is a merged version of the Spring 2016 and Winter 2017 versions of
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  <!-- Provide the basic links for the dataset. -->
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- - **Paper [optional]:** [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://arxiv.org/abs/1604.01696)
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  ## Uses
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  <!-- 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. -->
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- [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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- ### 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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- #### 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
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- <!-- 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. -->
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- #### Who are the annotators?
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- <!-- This section describes the people or systems who created the annotations. -->
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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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  **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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+ - **Paper:** [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://arxiv.org/abs/1604.01696)
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  ## Uses
 
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  <!-- 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. -->
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+ The dataset contains 98161 stories, each containing 5 sentences. Each instance or story includes a title, the original and the shuffled order of the sentences and a list of integers as gold order for evaluating prediction from a model.
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  ## Citation [optional]
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  **BibTeX:**
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+ @misc{mostafazadeh2016corpus,
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+ title={A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories},
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+ author={Nasrin Mostafazadeh and Nathanael Chambers and Xiaodong He and Devi Parikh and Dhruv Batra and Lucy Vanderwende and Pushmeet Kohli and James Allen},
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+ year={2016},
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+ eprint={1604.01696},
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+ archivePrefix={arXiv},
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+ primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
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+ }
 
 
 
 
 
 
 
 
 
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+ ## Dataset Card Authors
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+ Shawon Ashraf