Datasets:

lmqg
/

qg_dequad

	---
	license: cc-by-4.0
	pretty_name: GermanQuAD for question generation
	language: de
	multilinguality: monolingual
	size_categories: 10K<n<100K
	source_datasets: deepset/germanquad
	task_categories:
	- text-generation
	task_ids:
	- language-modeling
	tags:
	- question-generation
	---

	# Dataset Card for "lmqg/qg_dequad"

	## Dataset Description
	- Repository: [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
	- Paper: [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
	- Point of Contact: [Asahi Ushio](http://asahiushio.com/)

	### Dataset Summary
	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
	["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
	This is a modified version of [GermanQuAD](https://huggingface.co/datasets/deepset/germanquad) for question generation (QG) task.
	Since the original dataset only contains training/validation set, we manually sample test set from training set, which
	has no overlap in terms of the paragraph with the training set.

	### Supported Tasks and Leaderboards
	* `question-generation`: The dataset is assumed to be used to train a model for question generation.
	Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).

	### Languages
	Spanish (es)

	## Dataset Structure
	An example of 'train' looks as follows.
	```
	{
	'answer': 'elektromagnetischer Linearführungen',
	'question': 'Was kann den Verschleiß des seillosen Aufzuges minimieren?',
	'sentence': 'Im Rahmen der Forschungen an dem seillosen Aufzug wird ebenfalls an der Entwicklung elektromagnetischer Linearführungen gearbeitet, um den Verschleiß der seillosen Aufzugsanlage bei hohem Fahrkomfort zu minimieren.',
	'paragraph': "Aufzugsanlage\n\n=== Seilloser Aufzug ===\nAn der RWTH Aachen im Institut für Elektrische Maschinen wurde ein seilloser Aufzug entwickelt und ein Prototyp aufgebaut. Die Kabine wird hierbei durch z..."
	'sentence_answer': "Im Rahmen der Forschungen an dem seillosen Aufzug wird ebenfalls an der Entwicklung <hl> elektromagnetischer Linearführungen <hl> gearbeitet, um den Verschleiß der seillosen Aufzugsanlage bei...",
	'paragraph_answer': "Aufzugsanlage === Seilloser Aufzug === An der RWTH Aachen im Institut für Elektrische Maschinen wurde ein seilloser Aufzug entwickelt und ein Prototyp aufgebaut. Die Kabine wird hierbei durc...",
	'paragraph_sentence': "Aufzugsanlage === Seilloser Aufzug === An der RWTH Aachen im Institut für Elektrische Maschinen wurde ein seilloser Aufzug entwickelt und ein Prototyp aufgebaut. Die Kabine wird hierbei du..."
	}
	```
	## Data Fields
	The data fields are the same among all splits.
	- `question`: a `string` feature.
	- `paragraph`: a `string` feature.
	- `answer`: a `string` feature.
	- `sentence`: a `string` feature.
	- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
	- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
	- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.

	Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
	but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and
	`paragraph_sentence` feature is for sentence-aware question generation.

	### Data Splits

	\|train\|validation\|test \|
	\|----:\|---------:\|----:\|
	\|9314 \| 2204 \| 2204\|


	## Citation Information

	```
	@inproceedings{ushio-etal-2022-generative,
	title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
	author = "Ushio, Asahi and
	Alva-Manchego, Fernando and
	Camacho-Collados, Jose",
	booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
	month = dec,
	year = "2022",
	address = "Abu Dhabi, U.A.E.",
	publisher = "Association for Computational Linguistics",
	}
	```