# MASH-QA Dataset

MASH-QA is a dataset tailored for Question Answering (QA) tasks in the consumer health domain. It is designed to facilitate information extraction from long and comprehensive documents, focusing on answering generally non-factoid questions. Unlike traditional Machine Reading Comprehension (MRC) datasets that feature short, single-span answers, MASH-QA provides answers that are often multi-sentential and sourced from multiple spans of long context articles.

## Features of MASH-QA

- **Domain**: Consumer Health
- **Context Length**: Long healthcare articles
- **Question Type**: Generally non-factoid
- **Answer Type**: Multi-span, multi-sentence, and excerpted from various parts of the context.

## Dataset Details

MASH-QA enables researchers to:

- Explore multi-span extraction techniques for long contexts.
- Develop models capable of handling complex QA tasks with long, non-contiguous answers.

## Citation

If you use the MASH-QA dataset in your research, please cite the following paper:

@inproceedings{zhu2020question, title={Question Answering with Long Multiple-Span Answers}, author={Ming Zhu and Aman Ahuja and Da-Cheng Juan and Wei Wei and Chandan K. Reddy}, booktitle={Findings of the Association for Computational Linguistics: EMNLP 2020}, pages={3840--3849}, year={2020}, organization={Association for Computational Linguistics} }

## Paper

Ming Zhu, Aman Ahuja, Da-Cheng Juan, Wei Wei, and Chandan K. Reddy. 2020. [Question Answering with Long Multiple-Span Answers](https://doi.org/10.18653/v1/2020.findings-emnlp.342). In *Findings of the Association for Computational Linguistics: EMNLP 2020*. Association for Computational Linguistics, Online, 3840–3849.

## Dataset Access

The dataset is publicly available on GitHub. To access the dataset, visit the following link:

- **GitHub Repository**: [MASH-QA](https://github.com/mingzhu0527/MASHQA)

## Usage

The MASH-QA dataset is ideal for tasks such as:

- Long-form Question Answering
- Multi-span Extraction
- Healthcare Informatics Research

## License

Please refer to the GitHub repository for license details.

## Contact

For any questions or inquiries related to the dataset, please visit the [GitHub Issues](https://github.com/mingzhu0527/MASHQA/issues) page in the repository.