README.md

metadata

license: other
license_name: other
license_link: https://huggingface.co/datasets/microsoft/wiki_qa#licensing-information
task_categories:
  - question-answering
language:
  - en
  - fr
  - de
  - it
  - es
  - pt
pretty_name: mWikiQA
size_categories:
  - 100K<n<1M
configs:
  - config_name: en
    data_files:
      - split: train
        path: eng-train.jsonl
      - split: validation
        path: eng-dev.jsonl
      - split: test
        path: eng-test.jsonl
  - config_name: de
    data_files:
      - split: train
        path: deu-train.jsonl
      - split: validation
        path: deu-dev.jsonl
      - split: test
        path: deu-test.jsonl
  - config_name: fr
    data_files:
      - split: train
        path: fra-train.jsonl
      - split: validation
        path: fra-dev.jsonl
      - split: test
        path: fra-test.jsonl
  - config_name: it
    data_files:
      - split: train
        path: ita-train.jsonl
      - split: validation
        path: ita-dev.jsonl
      - split: test
        path: ita-test.jsonl
  - config_name: po
    data_files:
      - split: train
        path: por-train.jsonl
      - split: validation
        path: por-dev.jsonl
      - split: test
        path: por-test.jsonl
  - config_name: sp
    data_files:
      - split: train
        path: spa-train.jsonl
      - split: validation
        path: spa-dev.jsonl
      - split: test
        path: spa-test.jsonl
  - config_name: en_++
    data_files:
      - split: train
        path: eng-train.jsonl
      - split: validation
        path: eng-dev_no_allneg.jsonl
      - split: test
        path: eng-test_no_allneg.jsonl
  - config_name: de_++
    data_files:
      - split: train
        path: deu-train.jsonl
      - split: validation
        path: deu-dev_no_allneg.jsonl
      - split: test
        path: deu-test_no_allneg.jsonl
  - config_name: fr_++
    data_files:
      - split: train
        path: fra-train.jsonl
      - split: validation
        path: fra-dev_no_allneg.jsonl
      - split: test
        path: fra-test_no_allneg.jsonl
  - config_name: it_++
    data_files:
      - split: train
        path: ita-train.jsonl
      - split: validation
        path: ita-dev_no_allneg.jsonl
      - split: test
        path: ita-test_no_allneg.jsonl
  - config_name: po_++
    data_files:
      - split: train
        path: por-train.jsonl
      - split: validation
        path: por-dev_no_allneg.jsonl
      - split: test
        path: por-test_no_allneg.jsonl
  - config_name: sp_++
    data_files:
      - split: train
        path: spa-train.jsonl
      - split: validation
        path: spa-dev_no_allneg.jsonl
      - split: test
        path: spa-test_no_allneg.jsonl
  - config_name: en_clean
    data_files:
      - split: train
        path: eng-train.jsonl
      - split: validation
        path: eng-dev_clean.jsonl
      - split: test
        path: eng-test_clean.jsonl
  - config_name: de_clean
    data_files:
      - split: train
        path: deu-train.jsonl
      - split: validation
        path: deu-dev_clean.jsonl
      - split: test
        path: deu-test_clean.jsonl
  - config_name: fr_clean
    data_files:
      - split: train
        path: fra-train.jsonl
      - split: validation
        path: fra-dev_clean.jsonl
      - split: test
        path: fra-test_clean.jsonl
  - config_name: it_clean
    data_files:
      - split: train
        path: ita-train.jsonl
      - split: validation
        path: ita-dev_clean.jsonl
      - split: test
        path: ita-test_clean.jsonl
  - config_name: po_clean
    data_files:
      - split: train
        path: por-train.jsonl
      - split: validation
        path: por-dev_clean.jsonl
      - split: test
        path: por-test_clean.jsonl
  - config_name: sp_clean
    data_files:
      - split: train
        path: spa-train.jsonl
      - split: validation
        path: spa-dev_clean.jsonl
      - split: test
        path: spa-test_clean.jsonl

Dataset Description

mWikiQA is a translated version of WikiQA. It contains 3,047 questions sampled from Bing query logs. The candidate answer sentences are extracted from Wikipedia and then manually labeled to assess whether they are correct answers.

The dataset has been translated into five European languages: French, German, Italian, Portuguese, and Spanish, as described in this paper: "Datasets for Multilingual Answer Sentence Selection."

Splits:

For each language (English, French, German, Italian, Portuguese, and Spanish), we provide:

• train split
• validation split
• test split

In addition, the validation and the test splits are available also in the following preprocessed versions:

• ++: without questions with only negative answer candidates
• clean: without questions with only negative and only positive answer candidates

How to load them:

To use these splits, you can use the following snippet of code replacing [LANG] with a language identifier (en, fr, de, it, po, sp), and [VERSION] with the version identifier (++, clean)

from datasets import load_dataset

"""
if you want the default splits, replace [LANG] with an identifier in: en, fr, de, it, po, sp
dataset = load_dataset("mWikiQA", "[LANG]")
"""
# example:
italian_dataset = load_dataset("mWikiQA", "it")


"""
if you want the processed splits ("clean" and "no all negatives" sets), replace [LANG] with a language identifier and [VERSION] with "++" or "clean"
dataset = load_dataset("mWikiQA", "[LANG]_[VERSION]")
"""
# example:
italian_clean_dataset = load_dataset("mWikiQA", "it_clean")

Format:

Each example has the following format:

{
  'eid': 1214,
  'qid': 141,
  'cid': 0,
  'label': 1,
  'question': 'Was bedeutet Karma im Buddhismus?',
  'candidate': 'Karma (Sanskrit, auch karman, Pali: Kamma) bedeutet "Handlung" oder "Tun"; was auch immer man tut, sagt oder denkt, ist ein Karma.'
}

Where:

• eid: is the unique id of the example (question, candidate)
• qid: is the unique id of the question
• cid: is the unique id of the answer candidate
• label: identifies whether the answer candidate candidate is correct for the question (1 if correct, 0 otherwise)
• question: the question
• candidate: the answer candidate

Citation

If you find this dataset useful, please cite the following paper:

BibTeX:

@misc{gabburo2024datasetsmultilingualanswersentence,
      title={Datasets for Multilingual Answer Sentence Selection}, 
      author={Matteo Gabburo and Stefano Campese and Federico Agostini and Alessandro Moschitti},
      year={2024},
      eprint={2406.10172},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.10172}, 
}