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LogiQA

Paper

Title: LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Abstract: https://arxiv.org/abs/2007.08124

LogiQA is a dataset for testing human logical reasoning. It consists of 8,678 QA instances, covering multiple types of deductive reasoning. Results show that state- of-the-art neural models perform by far worse than human ceiling. The dataset can also serve as a benchmark for reinvestigating logical AI under the deep learning NLP setting.

Homepage: https://github.com/lgw863/LogiQA-dataset

Citation

@misc{liu2020logiqa,
    title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
    author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},
    year={2020},
    eprint={2007.08124},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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Checklist

For adding novel benchmarks/datasets to the library:

  • Is the task an existing benchmark in the literature?
    • Have you referenced the original paper that introduced the task?
    • If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?

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  • Is the "Main" variant of this task clearly denoted?
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