- Description:
Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog. Data instances consist of an interactive dialog between 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 (spans) 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.
Homepage: https://quac.ai/
Source code:
tfds.text.quac.Quac
Versions:
1.0.0
(default): Initial release.
Download size:
73.47 MiB
Dataset size:
298.04 MiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
83,568 |
'validation' |
7,354 |
- Features:
FeaturesDict({
'answers': Sequence({
'answer_start': tf.int32,
'text': Text(shape=(), dtype=tf.string),
}),
'background': Text(shape=(), dtype=tf.string),
'context': Text(shape=(), dtype=tf.string),
'followup': Text(shape=(), dtype=tf.string),
'orig_answer': FeaturesDict({
'answer_start': tf.int32,
'text': Text(shape=(), dtype=tf.string),
}),
'question': Text(shape=(), dtype=tf.string),
'section_title': Text(shape=(), dtype=tf.string),
'title': Text(shape=(), dtype=tf.string),
'yesno': Text(shape=(), dtype=tf.string),
})
Supervised keys (See
as_supervised
doc):('context', 'answers')
Citation:
@article{choi2018quac,
title={Quac: 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},
journal={arXiv preprint arXiv:1808.07036},
year={2018}
}
Figure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe):