Datasets:
matinf

Dataset Card for "matinf"

Dataset Summary

MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization. MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the merits held by MATINF.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

age_classification

  • Size of downloaded dataset files: 0.00 MB
  • Size of the generated dataset: 46.15 MB
  • Total amount of disk used: 46.15 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "description": "\"6个月的时候去儿宝检查,医生说宝宝的分胯动作做的不好,说最好去儿童医院看看,但我家宝宝很好,感觉没有什么不正常啊,请教一下,分胯做的不好,有什么不好吗?\"...",
    "id": 88016,
    "label": 0,
    "question": "医生说宝宝的分胯动作不好"
}

qa

  • Size of downloaded dataset files: 0.00 MB
  • Size of the generated dataset: 256.24 MB
  • Total amount of disk used: 256.24 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "answer": "\"我一个同学的孩子就是发现了肾积水,治疗了一段时间,结果还是越来越多,没办法就打掉了。虽然舍不得,但是还是要忍痛割爱,不然以后孩子真的有问题,大人和孩子都受罪。不过,这个最后的决定还要你自己做,毕竟是你的宝宝。,、、、、\"...",
    "id": 536714,
    "question": "孕5个月检查右侧肾积水孩子能要吗?"
}

summarization

  • Size of downloaded dataset files: 0.00 MB
  • Size of the generated dataset: 246.89 MB
  • Total amount of disk used: 246.89 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "description": "\"宝宝有中度HIE,但原因未查明,这是他出生后脸上红的几道,嘴唇深红近紫,请问这是像缺氧的表现吗?\"...",
    "id": 173649,
    "question": "宝宝脸上红的几道嘴唇深红近紫是像缺氧的表现吗?"
}

topic_classification

  • Size of downloaded dataset files: 0.00 MB
  • Size of the generated dataset: 208.89 MB
  • Total amount of disk used: 208.89 MB

An example of 'train' looks as follows.

{
    "description": "媳妇怀孕五个月了经检查右侧肾积水、过了半月左侧也出现肾积水、她要拿掉孩子、怎么办?",
    "id": 536714,
    "label": 8,
    "question": "孕5个月检查右侧肾积水孩子能要吗?"
}

Data Fields

The data fields are the same among all splits.

age_classification

  • question: a string feature.
  • description: a string feature.
  • label: a classification label, with possible values including 0-1岁 (0), 1-2岁 (1), 2-3岁 (2).
  • id: a int32 feature.

qa

  • question: a string feature.
  • answer: a string feature.
  • id: a int32 feature.

summarization

  • description: a string feature.
  • question: a string feature.
  • id: a int32 feature.

topic_classification

  • question: a string feature.
  • description: a string feature.
  • label: a classification label, with possible values including 产褥期保健 (0), 儿童过敏 (1), 动作发育 (2), 婴幼保健 (3), 婴幼心理 (4).
  • id: a int32 feature.

Data Splits

name train validation test
age_classification 134852 19323 38318
qa 747888 106842 213681
summarization 747888 106842 213681
topic_classification 613036 87519 175363

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

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

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@inproceedings{xu-etal-2020-matinf,
    title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
    author = "Xu, Canwen  and
      Pei, Jiaxin  and
      Wu, Hongtao  and
      Liu, Yiyu  and
      Li, Chenliang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.330",
    pages = "3586--3596",
}

Contributions

Thanks to @JetRunner for adding this dataset.

Models trained or fine-tuned on matinf

None yet