| # 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. | |