ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters
This dataset addresses the problem of improving QA systems for users by providing a resource that reflects diverse and complex real-world questions, though it is incremental as it builds on existing data collection methods.
The authors tackled the lack of real user question datasets for factoid question answering by introducing ComQA, a large dataset of 11,214 questions grouped into 4,834 paraphrase clusters, which captures challenging aspects like compositionality and temporal reasoning.
To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as compositionality, temporal reasoning, and comparisons. ComQA questions come from the WikiAnswers community QA platform, which typically contains questions that are not satisfactorily answerable by existing search engine technology. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA.