README.md

metadata

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_info:
  - config_name: dialog
    features:
      - name: documents
        sequence: string
      - name: document_id
        dtype: string
      - name: questions
        sequence:
          - name: question
            dtype: string
          - name: answer
            dtype: string
          - name: choice
            sequence: string
    splits:
      - name: train
        num_bytes: 2039779
        num_examples: 4885
      - name: test
        num_bytes: 646955
        num_examples: 1627
      - name: validation
        num_bytes: 611106
        num_examples: 1628
    download_size: 2073256
    dataset_size: 3297840
  - config_name: mixed
    features:
      - name: documents
        sequence: string
      - name: document_id
        dtype: string
      - name: questions
        sequence:
          - name: question
            dtype: string
          - name: answer
            dtype: string
          - name: choice
            sequence: string
    splits:
      - name: train
        num_bytes: 2710473
        num_examples: 3138
      - name: test
        num_bytes: 891579
        num_examples: 1045
      - name: validation
        num_bytes: 910759
        num_examples: 1046
    download_size: 3183780
    dataset_size: 4512811
configs:
  - config_name: dialog
    data_files:
      - split: train
        path: dialog/train-*
      - split: test
        path: dialog/test-*
      - split: validation
        path: dialog/validation-*
  - config_name: mixed
    data_files:
      - split: train
        path: mixed/train-*
      - split: test
        path: mixed/test-*
      - split: validation
        path: mixed/validation-*

Dataset Card for C3

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

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

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

[More Information Needed]

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

[More Information Needed]

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

[More Information Needed]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

Dataset provided for research purposes only. Please check dataset license for additional information.

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

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.