  We present two new large-scale datasets aimed at evaluating systems designed
to comprehend a natural language query and extract its answer from a large
corpus of text. The Quasar-S dataset consists of 37000 cloze-style
(fill-in-the-gap) queries constructed from definitions of software entity tags
on the popular website Stack Overflow. The posts and comments on the website
serve as the background corpus for answering the cloze questions. The Quasar-T
dataset consists of 43000 open-domain trivia questions and their answers
obtained from various internet sources. ClueWeb09 serves as the background
corpus for extracting these answers. We pose these datasets as a challenge for
two related subtasks of factoid Question Answering: (1) searching for relevant
pieces of text that include the correct answer to a query, and (2) reading the
retrieved text to answer the query. We also describe a retrieval system for
extracting relevant sentences and documents from the corpus given a query, and
include these in the release for researchers wishing to only focus on (2). We
evaluate several baselines on both datasets, ranging from simple heuristics to
powerful neural models, and show that these lag behind human performance by
16.4% and 32.1% for Quasar-S and -T respectively. The datasets are available at
https://github.com/bdhingra/quasar .
