Dataset Card for "lmqg/qag_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.

{
  "paragraph": "I would hope that Phylicia Rashad would apologize now that @missjillscott has! You cannot discount 30 victims who come with similar stories.— JDWhitner (@JDWhitner) July 7, 2015",
  "questions": [ "what should phylicia rashad do now?", "how many victims have come forward?" ],
  "answers": [ "apologize", "30" ],
  "questions_answers": "Q: what should phylicia rashad do now?, A: apologize Q: how many victims have come forward?, A: 30"
}

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.
• questions_answers: 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",
}