Modern search engines have made dramatic progress in answering questions about facts, such as those that might be retrieved or directly inferred from a knowledge base. However, many other real user questions are more complex, such as requests for opinions, explanations, instructions or advice for a particular situation, and are still largely beyond the competence of the computer systems. As conversational agents become more popular, QA systems are increasingly expected to handle such complex questions, and to do so in (nearly) real-time, as the searcher is unlikely to wait longer than a minute or two for an answer. One way to overcome some of the challenges in complex question answering is crowdsourcing. We explore two ways crowdsourcing can assist a question answering system that operates in (near) real time: by providing answer validation, which could be used to filter or re-rank the candidate answers, and by creating the answer candidates directly. In this paper we present CRQA, a crowd-powered, near real-time automatic question answering system for complex informational tasks, that incorporates a crowdsourcing module for augmenting and validating the candidate answers. The crowd input, obtained in real-time, is integrated into CRQA via a learning-to-rank model, to select the final system answer. Our large-scale experiments, performed on a live stream of real users questions, show that even within a one minute time limit, CRQA can produce answers of high quality. The returned answers are judged to be significantly better compared to the automatic system alone, and even are often preferred to answers posted days later in the original community question answering site. Our findings can be useful for developing hybrid human-computer systems for automatic question answering and conversational agents.
Published Date: 2016-11-03
Registration: ISBN 978-1-57735-774-2