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README.md
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---
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pretty_name: ScandiQA
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language:
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- da
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- sv
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- no
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license:
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- cc-by-sa-4.0
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multilinguality:
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- multilingual
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size_categories:
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- 1K<n<10K
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source_datasets:
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- mkqa|natural_questions
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task_categories:
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- question-answering
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task_ids:
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- extractive-qa
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---
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# Dataset Card for ScandiQA
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## Dataset Description
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- **Repository:** <https://github.com/alexandrainst/scandi-qa>
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- **Point of Contact:** [Dan Saattrup Nielsen](mailto:[email protected])
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- **Size of downloaded dataset files:** 69 MB
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- **Size of the generated dataset:** 67 MB
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- **Total amount of disk used:** 136 MB
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### Dataset Summary
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ScandiQA is a dataset of questions and answers in the Danish, Norwegian, and Swedish
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languages. All samples come from the Natural Questions (NQ) dataset, which is a large
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question answering dataset from Google searches. The Scandinavian questions and answers
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come from the MKQA dataset, where 10,000 NQ samples were manually translated into,
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among others, Danish, Norwegian, and Swedish. However, this did not include a
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translated context, hindering the training of extractive question answering models.
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We merged the NQ dataset with the MKQA dataset, and extracted contexts as either "long
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answers" from the NQ dataset, being the paragraph in which the answer was found, or
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otherwise we extract the context by locating the paragraphs which have the largest
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cosine similarity to the question, and which contains the desired answer.
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Further, many answers in the MKQA dataset were "language normalised": for instance, all
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date answers were converted to the format "YYYY-MM-DD", meaning that in most cases
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these answers are not appearing in any paragraphs. We solve this by extending the MKQA
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answers with plausible "answer candidates", being slight perturbations or translations
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of the answer.
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With the contexts extracted, we translated these to Danish, Swedish and Norwegian using
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the DeepL translation service for Danish and Swedish, and the Google Translation
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service for Norwegian. After translation we ensured that the Scandinavian answers do
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indeed occur in the translated contexts.
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As we are filtering the MKQA samples at both the "merging stage" and the "translation
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stage", we are not able to fully convert the 10,000 samples to the Scandinavian
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languages, and instead get roughly 8,000 samples per language. These have further been
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split into a training, validation and test split, with the former two containing
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roughly 750 samples. The splits have been created in such a way that the proportion of
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samples without an answer is roughly the same in each split.
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### Supported Tasks and Leaderboards
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Training machine learning models for extractive question answering is the intended task
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for this dataset. No leaderboard is active at this point.
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### Languages
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The dataset is available in Danish (`da`), Swedish (`sv`) and Norwegian (`no`).
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## Dataset Structure
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### Data Instances
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- **Size of downloaded dataset files:** 69 MB
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- **Size of the generated dataset:** 67 MB
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- **Total amount of disk used:** 136 MB
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An example from the `train` split of the `da` subset looks as follows.
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```
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{
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'example_id': 123,
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'question': 'Er dette en test?',
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'answer': 'Dette er en test',
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'answer_start': 0,
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'context': 'Dette er en testkontekst.',
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'answer_en': 'This is a test',
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'answer_start_en': 0,
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'context_en': "This is a test",
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'title_en': 'Train test'
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}
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```
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### Data Fields
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The data fields are the same among all splits.
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- `example_id`: an `int64` feature.
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- `question`: a `string` feature.
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- `answer`: a `string` feature.
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- `answer_start`: an `int64` feature.
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- `context`: a `string` feature.
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- `answer_en`: a `string` feature.
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- `answer_start_en`: an `int64` feature.
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- `context_en`: a `string` feature.
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- `title_en`: a `string` feature.
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### Data Splits
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| name | train | validation | test |
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|----------|------:|-----------:|-----:|
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| da | 6311 | 749 | 750 |
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| sv | 6299 | 750 | 749 |
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| no | 6314 | 749 | 750 |
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## Dataset Creation
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### Curation Rationale
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The Scandinavian languages does not have any gold standard question answering dataset.
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This is not quite gold standard, but the fact both the questions and answers are all
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manually translated, it is a solid silver standard dataset.
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### Source Data
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The original data was collected from the [MKQA](https://github.com/apple/ml-mkqa/) and
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[Natural Questions](https://ai.google.com/research/NaturalQuestions) datasets from
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Apple and Google, respectively.
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## Additional Information
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### Dataset Curators
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[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
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Institute](https://alexandra.dk/) curated this dataset.
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### Licensing Information
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The dataset is licensed under the [CC BY-SA 4.0
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license](https://creativecommons.org/licenses/by-sa/4.0/).
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