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allenai/quac

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Possible licence conflict

The declared licence (mit) may conflict with a source dataset's reported share-alike licence (cc-by-sa-3.0).

Declared licence: mit, from the tagEvidence: README.md at 2c1b73f
Licence files: No licence file at the top level of the repositoryEvidence: file list at 2c1b73f
Declared sources: 1
Access gated on Hugging Face: No

Red marks the licence the signal is based on. Each cell links to what was checked: a file, a file list or a Hub record.

Sources listed in the card metadata

wikipediacc-by-sa-3.0Share-alikeEvidence: README.md at 97a0b05

Tasks

Citation text from the card

@inproceedings{choi-etal-2018-quac,
    title = "{Q}u{AC}: Question Answering in Context",
    author = "Choi, Eunsol  and
      He, He  and
      Iyyer, Mohit  and
      Yatskar, Mark  and
      Yih, Wen-tau  and
      Choi, Yejin  and
      Liang, Percy  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1241",
    doi = "10.18653/v1/D18-1241",
    pages = "2174--2184",
    abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting

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dataset_licence({ "id": "allenai/quac" })
signal: "conflict"
stated_licence: "mit"
sources: 1