SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
This provides a new benchmark for machine comprehension, addressing the need for datasets that simulate real-world QA pipelines, though it is incremental as it builds on existing QA data.
The authors introduced SearchQA, a large-scale question-answering dataset with over 140k pairs augmented by 49.6 text snippets on average from web searches, and found a meaningful performance gap between human and machine baselines.
We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google. Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.