Dataset Card for sberquad

Dataset Summary

Sber Question Answering Dataset (SberQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Russian original analogue presented in Sberbank Data Science Journey 2017.

Supported Tasks and Leaderboards

[Needs More Information]

Languages

Russian

Dataset Structure

Data Instances

{
    "context": "Первые упоминания о строении человеческого тела встречаются в Древнем Египте...",
    "id": 14754,
    "qas": [
        {
            "id": 60544,
            "question": "Где встречаются первые упоминания о строении человеческого тела?",
            "answers": [{"answer_start": 60, "text": "в Древнем Египте"}],
        }
    ]
}

Data Fields

• id: a int32 feature
• title: a string feature
• context: a string feature
• question: a string feature
• answers: a dictionary feature containing:
  • text: a string feature
  • answer_start: a int32 feature

Data Splits

name	train	validation	test
plain_text	45328	5036	23936

Dataset Creation

Curation Rationale

[Needs More Information]

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

[Needs More Information]

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{sberquad,
doi       = {10.1007/978-3-030-58219-7_1},
author    = {Pavel Efimov and
             Andrey Chertok and
             Leonid Boytsov and
             Pavel Braslavski},
title     = {SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis},
booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction},
year      = {2020},
publisher = {Springer International Publishing},
pages     = {3--15}
}

Contributions

Thanks to @alenusch for adding this dataset.