Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
German
Size:
10K<n<100K
ArXiv:
Tags:
question-generation
License:
| 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: question-generation | |
| task_ids: 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:** [TBA](TBA) | |
| - **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"](paper_link). | |
| 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: {A} {U}nified {B}enchmark and {E}valuation", | |
| 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", | |
| } | |
| ``` |