ai2_arc

  • Description:

A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.

FeaturesDict({
    'answerKey': ClassLabel(shape=(), dtype=tf.int64, num_classes=5),
    'choices': Sequence({
        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=5),
        'text': Text(shape=(), dtype=tf.string),
    }),
    'id': Text(shape=(), dtype=tf.string),
    'question': Text(shape=(), dtype=tf.string),
})
@article{allenai:arc,
      author    = {Peter Clark  and Isaac Cowhey and Oren Etzioni and Tushar Khot and
                    Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
      title     = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
      journal   = {arXiv:1803.05457v1},
      year      = {2018},
}

ai2_arc/ARC-Challenge (default config)

  • Config description: Challenge Set of 2590 "hard" questions (those that both a retrieval and a co-occurrence method fail to answer correctly)

  • Dataset size: 939.91 KiB

  • Splits:

Split Examples
'test' 1,172
'train' 1,119
'validation' 299

ai2_arc/ARC-Easy

  • Config description: Easy Set of 5197 questions for the ARC Challenge.

  • Dataset size: 1.63 MiB

  • Splits:

Split Examples
'test' 2,376
'train' 2,251
'validation' 570