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Dataset Card for "lmqg/qg_dequad"
Dataset Summary
This is a subset of QG-Bench, a unified question generation benchmark proposed in "Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference". This is a modified version of 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
: astring
feature.paragraph
: astring
feature.answer
: astring
feature.sentence
: astring
feature.paragraph_answer
: astring
feature, which is same as the paragraph but the answer is highlighted by a special token<hl>
.paragraph_sentence
: astring
feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token<hl>
.sentence_answer
: astring
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",
}
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