Dataset Card for "lmqg/qg_zhquad"

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 Chinese SQuAD 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.

Please see the original repository (https://github.com/junzeng-pluto/ChineseSquad) for more details.

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

Italian (it)

Dataset Structure

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
59977	8236	8236

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",
}