# OpenBookQA

### Paper

Title: `Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering`

Abstract: https://arxiv.org/abs/1809.02789

OpenBookQA is a question-answering dataset modeled after open book exams for
assessing human understanding of a subject. It consists of 5,957 multiple-choice
elementary-level science questions (4,957 train, 500 dev, 500 test), which probe
the understanding of a small “book” of 1,326 core science facts and the application
of these facts to novel situations. For training, the dataset includes a mapping
from each question to the core science fact it was designed to probe. Answering
OpenBookQA questions requires additional broad common knowledge, not contained
in the book. The questions, by design, are answered incorrectly by both a retrieval-
based algorithm and a word co-occurrence algorithm.

Homepage: https://allenai.org/data/open-book-qa


### Citation

```
@inproceedings{OpenBookQA2018,
    title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},
    author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},
    booktitle={EMNLP},
    year={2018}
}
```

### Groups and Tasks

#### Groups

* Not part of a group yet

#### Tasks

* `openbookqa`

### Checklist

For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
  * [ ] Have you referenced the original paper that introduced the task?
  * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?


If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?