lmqg/qg_tweetqa

Dataset Card for "lmqg/qg_tweetqa"

Dataset Description

• —Repository: https://github.com/asahi417/lm-question-generation
• —Paper: https://arxiv.org/abs/2210.03992
• —Point of Contact: Asahi Ushio

Dataset Summary

This is the question & answer generation dataset based on the tweet_qa. The test set of the original data is not publicly released, so we randomly sampled test questions from the training set.

Supported Tasks and Leaderboards

• —question-answer-generation: The dataset is assumed to be used to train a model for question & answer 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

English (en)

Dataset Structure

An example of 'train' looks as follows.

{
  'answer': 'vine',
  'paragraph_question': 'question: what site does the link take you to?, context:5 years in 5 seconds. Darren Booth (@darbooth) January 25, 2013',
  'question': 'what site does the link take you to?',
  'paragraph': '5 years in 5 seconds. Darren Booth (@darbooth) January 25, 2013'
}

The data fields are the same among all splits.

• —questions: a list of string features.
• —answers: a list of string features.
• —paragraph: a string feature.
• —question_answer: a string feature.

Data Splits

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",
}