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To bridge the gap between the capabilities of the state-of-the-art in factoidquestion answering (QA) and what users ask, we need large datasets of real userquestions that capture the various question phenomena users are interested in,and the diverse ways in which these questions are formulated. We introduceComQA, a large dataset of real user questions that exhibit differentchallenging aspects such as compositionality, temporal reasoning, andcomparisons. ComQA questions come from the WikiAnswers community QA platform,which typically contains questions that are not satisfactorily answerable byexisting search engine technology. Through a large crowdsourcing effort, weclean the question dataset, group questions into paraphrase clusters, andannotate clusters with their answers. ComQA contains 11,214 questions groupedinto 4,834 paraphrase clusters. We detail the process of constructing ComQA,including the measures taken to ensure its high quality while making effectiveuse of crowdsourcing. We also present an extensive analysis of the dataset andthe results achieved by state-of-the-art systems on ComQA, demonstrating thatour dataset can be a driver of future research on QA.

ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters