language:ind license:unknown question-answering region:us
  XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across
languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around
the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. All the details about the
creation of XCOPA and the implementation of the baselines are available in the paper.
@inproceedings{ponti2020xcopa,
  title={{XCOPA: A} Multilingual Dataset for Causal Commonsense Reasoning},
  author={Edoardo M. Ponti, Goran Glava\v{s}, Olga Majewska, Qianchu Liu, Ivan Vuli\'{c} and Anna Korhonen},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2020},
  url={https://ducdauge.github.io/files/xcopa.pdf}
}
@inproceedings{roemmele2011choice,
  title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning},
  author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S},
  booktitle={2011 AAAI Spring Symposium Series},
  year={2011},
  url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF},
}
---
license: unknown
tags:
- question-answering
language:
- ind
---

# xcopa

XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning

The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across

languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around

the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. All the details about the

creation of XCOPA and the implementation of the baselines are available in the paper.

## Dataset Usage

Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.

## Citation

```
@inproceedings{ponti2020xcopa,
  title={{XCOPA: A} Multilingual Dataset for Causal Commonsense Reasoning},
  author={Edoardo M. Ponti, Goran Glava{s}, Olga Majewska, Qianchu Liu, Ivan Vuli'{c} and Anna Korhonen},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2020},
  url={https://ducdauge.github.io/files/xcopa.pdf}
}
@inproceedings{roemmele2011choice,
  title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning},
  author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S},
  booktitle={2011 AAAI Spring Symposium Series},
  year={2011},
  url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF},
}
```

## License

Unknown

## Homepage

[https://github.com/cambridgeltl/xcopa](https://github.com/cambridgeltl/xcopa)

### NusaCatalogue

For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)