Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
Russian
Size:
10K<n<100K
ArXiv:
Tags:
question-generation
License:
Create README.md
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asahi417
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README.md
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1 |
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---
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license: cc-by-4.0
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pretty_name: SberQuAD for question generation
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language: ru
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multilinguality: monolingual
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size_categories: 10K<n<100K
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source_datasets: deepset/germanquad
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task_categories:
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- text-generation
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task_ids:
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- language-modeling
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tags:
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- question-generation
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---
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# Dataset Card for "lmqg/qg_ruquad"
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## Dataset Description
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- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
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- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
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- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
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### Dataset Summary
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This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
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["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
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This is a modified version of [SberQuaD](https://huggingface.co/datasets/sberquad) for question generation (QG) task.
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Since the original dataset only contains training/validation set, we manually sample test set from training set, which
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has no overlap in terms of the paragraph with the training set.
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### Supported Tasks and Leaderboards
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* `question-generation`: The dataset is assumed to be used to train a model for question generation.
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Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
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### Languages
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Russian (ru)
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## Dataset Structure
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An example of 'train' looks as follows.
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```
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{
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'answer': 'известковыми выделениями сине-зелёных водорослей',
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'question': 'чем представлены органические остатки?',
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'sentence': 'Они представлены известковыми выделениями сине-зелёных водорослей , ходами червей, остатками кишечнополостных.'
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'paragraph': "В протерозойских отложениях органические остатки встречаются намного чаще, чем в архейских. Они представлены..."
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'sentence_answer': "Они представлены <hl> известковыми выделениями сине-зелёных водорослей <hl> , ход...",
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'paragraph_answer': "В протерозойских отложениях органические остатки встречаются намного чаще, чем в архейских. Они представлены <hl> известковыми выделениям...",
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'paragraph_sentence': "В протерозойских отложениях органические остатки встречаются намного чаще, чем в архейских. <hl> Они представлены известковыми выделениями сине-зелёных водорослей , ходами червей, остатками кишечнополостных. <hl> Кроме..."
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}
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```
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The data fields are the same among all splits.
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- `question`: a `string` feature.
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- `paragraph`: a `string` feature.
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- `answer`: a `string` feature.
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- `sentence`: a `string` feature.
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- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
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- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
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- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
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Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
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but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and
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`paragraph_sentence` feature is for sentence-aware question generation.
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## Data Splits
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-
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|train|validation|test |
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|----:|---------:|----:|
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| 45327 | 5036 |23936 |
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## Citation Information
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```
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@inproceedings{ushio-etal-2022-generative,
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title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
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author = "Ushio, Asahi and
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Alva-Manchego, Fernando and
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Camacho-Collados, Jose",
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
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month = dec,
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year = "2022",
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address = "Abu Dhabi, U.A.E.",
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publisher = "Association for Computational Linguistics",
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}
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```
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