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We present two new large-scale datasets aimed at evaluating systems designedto comprehend a natural language query and extract its answer from a largecorpus of text. The Quasar-S dataset consists of 37000 cloze-style(fill-in-the-gap) queries constructed from definitions of software entity tagson the popular website Stack Overflow. The posts and comments on the websiteserve as the background corpus for answering the cloze questions. The Quasar-Tdataset consists of 43000 open-domain trivia questions and their answersobtained from various internet sources. ClueWeb09 serves as the backgroundcorpus for extracting these answers. We pose these datasets as a challenge fortwo related subtasks of factoid Question Answering: (1) searching for relevantpieces of text that include the correct answer to a query, and (2) reading theretrieved text to answer the query. We also describe a retrieval system forextracting relevant sentences and documents from the corpus given a query, andinclude these in the release for researchers wishing to only focus on (2). Weevaluate several baselines on both datasets, ranging from simple heuristics topowerful neural models, and show that these lag behind human performance by16.4% and 32.1% for Quasar-S and -T respectively. The datasets are available athttps://github.com/bdhingra/quasar .

Quasar: Datasets for Question Answering by Search and Reading