Datasets:
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- zh
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: c3
pretty_name: C3
Dataset Card for C3
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage:
- Repository: link
- Paper:
- Leaderboard:
- Point of Contact:
Dataset Summary
Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.
Supported Tasks and Leaderboards
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Languages
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Dataset Structure
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Data Instances
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Data Fields
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Data Splits
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Dataset Creation
Curation Rationale
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Source Data
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Initial Data Collection and Normalization
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Who are the source language producers?
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Annotations
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Annotation process
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Who are the annotators?
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Personal and Sensitive Information
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Considerations for Using the Data
Social Impact of Dataset
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Discussion of Biases
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Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
Additional Information
Dataset Curators
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Licensing Information
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Citation Information
@article{sun2019investigating,
title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},
author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
url={https://arxiv.org/abs/1904.09679v3}
}
Contributions
Thanks to @Narsil for adding this dataset.