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--- |
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language: |
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- en |
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bigbio_language: |
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- English |
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license: unknown |
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multilinguality: monolingual |
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bigbio_license_shortname: UNKNOWN |
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pretty_name: BIOMRC |
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homepage: https://github.com/PetrosStav/BioMRC_code |
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bigbio_pubmed: True |
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bigbio_public: True |
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bigbio_tasks: |
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- QUESTION_ANSWERING |
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--- |
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# Dataset Card for BIOMRC |
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## Dataset Description |
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- **Homepage:** https://github.com/PetrosStav/BioMRC_code |
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- **Pubmed:** True |
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- **Public:** True |
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- **Tasks:** QA |
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We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the |
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previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the |
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new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating |
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that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is |
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also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new |
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BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or |
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surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different |
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sizes, also releasing our code, and providing a leaderboard. |
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## Citation Information |
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``` |
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@inproceedings{pappas-etal-2020-biomrc, |
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title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension", |
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author = "Pappas, Dimitris and |
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Stavropoulos, Petros and |
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Androutsopoulos, Ion and |
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McDonald, Ryan", |
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booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing", |
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month = jul, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2020.bionlp-1.15", |
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pages = "140--149", |
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} |
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``` |
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