SQuAD: 100,000+ Questions for Machine Comprehension of Text
This provides a benchmark dataset for advancing machine comprehension research, though it is incremental as it builds on existing data collection methods.
The authors tackled the problem of machine reading comprehension by creating SQuAD, a dataset of over 100,000 questions based on Wikipedia articles, and achieved an F1 score of 51.0% with a logistic regression model, significantly improving over a baseline of 20%.
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