ai2_arc_with_ir

• 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.

Compared to the original dataset, this adds context sentences obtained through information retrieval in the same way as UnifiedQA (see: https://arxiv.org/abs/2005.00700 ).

• Additional Documentation: Explore on Papers With Code

• Homepage: https://allenai.org/data/arc

• Source code: tfds.datasets.ai2_arc_with_ir.Builder

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: 3.68 MiB

• Auto-cached (documentation): Yes

• Feature structure:

FeaturesDict({
    'answerKey': ClassLabel(shape=(), dtype=int64, num_classes=5),
    'choices': Sequence({
        'label': ClassLabel(shape=(), dtype=int64, num_classes=5),
        'text': Text(shape=(), dtype=string),
    }),
    'id': Text(shape=(), dtype=string),
    'paragraph': Text(shape=(), dtype=string),
    'question': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
answerKey	ClassLabel		int64
choices	Sequence
choices/label	ClassLabel		int64
choices/text	Text		string
id	Text		string
paragraph	Text		string
question	Text		string

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Citation:

@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},
}
@article{2020unifiedqa,
    title={UnifiedQA: Crossing Format Boundaries With a Single QA System},
    author={D. Khashabi and S. Min and T. Khot and A. Sabhwaral and O. Tafjord and P. Clark and H. Hajishirzi},
    journal={arXiv preprint},
    year={2020}
}

ai2_arc_with_ir/ARC-Challenge-IR (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: 3.76 MiB

• Splits:

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

• Examples (tfds.as_dataframe):

ai2_arc_with_ir/ARC-Easy-IR

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

• Dataset size: 7.49 MiB

• Splits:

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

• Examples (tfds.as_dataframe):