· 2016
SQuAD: 100,000+ Questions for Machine Comprehension of Text
- 6,364citations
- 2016year
Short summary
The Stanford Question Answering Dataset (SQuAD) introduces over 100,000 crowd-sourced questions on Wikipedia articles, where answers are text segments from the passages, setting a new benchmark for machine reading comprehension.
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Key points
- Introduces SQuAD, a dataset with over 100,000 crowd-sourced questions on Wikipedia articles.
- Answers are defined as contiguous segments of text from the corresponding reading passages.
- A logistic regression model achieved an F1 score of 51.0%, a 31-point improvement over a simple baseline.
- Human performance on the dataset is 86.8% F1, indicating significant room for improvement in machine comprehension.
AI-generated from the title and abstract; the full text is not read.
Abstract
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
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science