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README.md
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## Dataset Creation
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### Curation Rationale
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[More Information Needed]
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### Source Data
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#### Initial Data Collection and Normalization
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#### Who are the source language producers?
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[More Information Needed]
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### Annotations
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#### Annotation process
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#### Who are the annotators?
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[More Information Needed]
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### Personal and Sensitive Information
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[More Information Needed]
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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[More Information Needed]
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### Licensing Information
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HuCoPA is released under the BSD 2-Clause License.
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If you use this resource or any part of its documentation, please refer to:
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and to:
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Roemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011.
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## Dataset Creation
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### Source Data
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#### Initial Data Collection and Normalization
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The data is a translation of the content of the CoPA corpus. Each sentence was translated by a human translator. Each translation was manually checked and further refined by another annotator.
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### Annotations
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#### Annotation process
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The instances initially inherited their original labels from the CoPA dataset. Each instance was annotated by a human annotator. If the original label and the human annotator's label did not match, we manually curated the instance and assigned a final label to that. This step was necessary to ensure that the causal realationship had not been changed or lost during the translation process.
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#### Who are the annotators?
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The translators were native Hungarian speakers with English proficiency. The annotators were university students with some linguistic background.
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## Additional Information
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### Licensing Information
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HuCoPA is released under the BSD 2-Clause License.
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If you use this resource or any part of its documentation, please refer to:
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Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Vadász, T. (2022) HuLU: magyar nyelvű benchmark adatbázis
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kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. (in press)
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```
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@inproceedings{ligetinagy2022hulu,
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title={uLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából},
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author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Vadász, T.},
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booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
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year={2022}
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}
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```
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and to:
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Roemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011.
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