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Datasets:
google-research-datasets
/
tydiqa

Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
Arabic
Bengali
English
Size:
100K - 1M
Libraries:
Datasets
Dask
License:
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metadata
pretty_name: TyDi QA
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - ar
  - bn
  - en
  - fi
  - id
  - ja
  - ko
  - ru
  - sw
  - te
  - th
license:
  - apache-2.0
multilinguality:
  - multilingual
size_categories:
  - unknown
source_datasets:
  - extended|wikipedia
task_categories:
  - question-answering
task_ids:
  - extractive-qa
paperswithcode_id: tydi-qa
dataset_info:
  - config_name: primary_task
    features:
      - name: passage_answer_candidates
        sequence:
          - name: plaintext_start_byte
            dtype: int32
          - name: plaintext_end_byte
            dtype: int32
      - name: question_text
        dtype: string
      - name: document_title
        dtype: string
      - name: language
        dtype: string
      - name: annotations
        sequence:
          - name: passage_answer_candidate_index
            dtype: int32
          - name: minimal_answers_start_byte
            dtype: int32
          - name: minimal_answers_end_byte
            dtype: int32
          - name: yes_no_answer
            dtype: string
      - name: document_plaintext
        dtype: string
      - name: document_url
        dtype: string
    splits:
      - name: train
        num_bytes: 5550574617
        num_examples: 166916
      - name: validation
        num_bytes: 484380443
        num_examples: 18670
    download_size: 1953887429
    dataset_size: 6034955060
  - config_name: secondary_task
    features:
      - name: id
        dtype: string
      - name: title
        dtype: string
      - name: context
        dtype: string
      - name: question
        dtype: string
      - name: answers
        sequence:
          - name: text
            dtype: string
          - name: answer_start
            dtype: int32
    splits:
      - name: train
        num_bytes: 52948607
        num_examples: 49881
      - name: validation
        num_bytes: 5006461
        num_examples: 5077
    download_size: 1953887429
    dataset_size: 57955068

Dataset Card for "tydiqa"

Table of Contents

Dataset Description

Dataset Summary

TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language expresses -- such that we expect models performing well on this set to generalize across a large number of the languages in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without the use of translation (unlike MLQA and XQuAD).

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

primary_task

  • Size of downloaded dataset files: 1.95 GB
  • Size of the generated dataset: 6.04 GB
  • Total amount of disk used: 7.99 GB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "annotations": {
        "minimal_answers_end_byte": [-1, -1, -1],
        "minimal_answers_start_byte": [-1, -1, -1],
        "passage_answer_candidate_index": [-1, -1, -1],
        "yes_no_answer": ["NONE", "NONE", "NONE"]
    },
    "document_plaintext": "\"\\nรองศาสตราจารย์[1] หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร  (22 กันยายน 2495 -) ผู้ว่าราชการกรุงเทพมหานครคนที่ 15 อดีตรองหัวหน้าพรรคปร...",
    "document_title": "หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร",
    "document_url": "\"https://th.wikipedia.org/wiki/%E0%B8%AB%E0%B8%A1%E0%B9%88%E0%B8%AD%E0%B8%A1%E0%B8%A3%E0%B8%B2%E0%B8%8A%E0%B8%A7%E0%B8%87%E0%B8%...",
    "language": "thai",
    "passage_answer_candidates": "{\"plaintext_end_byte\": [494, 1779, 2931, 3904, 4506, 5588, 6383, 7122, 8224, 9375, 10473, 12563, 15134, 17765, 19863, 21902, 229...",
    "question_text": "\"หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร เรียนจบจากที่ไหน ?\"..."
}

secondary_task

  • Size of downloaded dataset files: 1.95 GB
  • Size of the generated dataset: 58.03 MB
  • Total amount of disk used: 2.01 GB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "answers": {
        "answer_start": [394],
        "text": ["بطولتين"]
    },
    "context": "\"أقيمت البطولة 21 مرة، شارك في النهائيات 78 دولة، وعدد الفرق التي فازت بالبطولة حتى الآن 8 فرق، ويعد المنتخب البرازيلي الأكثر تت...",
    "id": "arabic-2387335860751143628-1",
    "question": "\"كم عدد مرات فوز الأوروغواي ببطولة كاس العالم لكرو القدم؟\"...",
    "title": "قائمة نهائيات كأس العالم"
}

Data Fields

The data fields are the same among all splits.

primary_task

  • passage_answer_candidates: a dictionary feature containing:
    • plaintext_start_byte: a int32 feature.
    • plaintext_end_byte: a int32 feature.
  • question_text: a string feature.
  • document_title: a string feature.
  • language: a string feature.
  • annotations: a dictionary feature containing:
    • passage_answer_candidate_index: a int32 feature.
    • minimal_answers_start_byte: a int32 feature.
    • minimal_answers_end_byte: a int32 feature.
    • yes_no_answer: a string feature.
  • document_plaintext: a string feature.
  • document_url: a string feature.

secondary_task

  • id: a string 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
primary_task 166916 18670
secondary_task 49881 5077

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

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

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information 

@article{tydiqa,
title   = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author  = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year    = {2020},
journal = {Transactions of the Association for Computational Linguistics}
}

Contributions

Thanks to @thomwolf, @albertvillanova, @lewtun, @patrickvonplaten for adding this dataset.