# SQuAD: 100,000+ Questions for Machine Comprehension of Text

arXiv: https://arxiv.org/abs/1606.05250 · HTML: https://mlsanity.com/paper/1606.05250/

## Key facts

- Cited by 4,148 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2016.

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## Abstract (arXiv)

We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com