language:ind question-answering region:us
FacQA: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article.
Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the
corresponding short passage. There are six categories of questions: date, location, name,
organization, person, and quantitative.
@inproceedings{purwarianti2007machine,
  title={A Machine Learning Approach for Indonesian Question Answering System},
  author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa},
  booktitle={Proceedings of Artificial Intelligence and Applications },
  pages={573--578},
  year={2007}
}
---
tags:
- question-answering
language:
- ind
---

# facqa

FacQA: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article.

Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the

corresponding short passage. There are six categories of questions: date, location, name,

organization, person, and quantitative.

## Dataset Usage

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

## Citation

```
@inproceedings{purwarianti2007machine,
  title={A Machine Learning Approach for Indonesian Question Answering System},
  author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa},
  booktitle={Proceedings of Artificial Intelligence and Applications },
  pages={573--578},
  year={2007}
}
```

## License

CC-BY-SA 4.0

## Homepage

[https://github.com/IndoNLP/indonlu](https://github.com/IndoNLP/indonlu)

### NusaCatalogue

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