Towards natural question guided search

Web search is generally motivated by an information need. Since asking well-formulated questions is the fastest and the most natural way to obtain information for human beings, almost all queries posed to search engines correspond to some underlying questions, which reflect the user's information need. Accurate determination of these questions may substantially improve the quality of search results and usability of search interfaces. In this paper, we propose a new framework for question-guided search, in which a retrieval system would automatically generate potentially interesting questions to users based on the search results of a query. Since the answers to such questions are known to exist in the search results, these questions can potentially guide users directly to the answers that they are looking for, eliminating the need to scan the documents in the result list. Moreover, in case of imprecise or ambiguous queries, automatically generated questions can naturally engage users into a feedback cycle to refine their information need and guide them towards their search goals. Implementation of the proposed strategy raises new challenges in content indexing, question generation, ranking and feedback. We propose new methods to address these challenges and evaluated them with a prototype system on a subset of Wikipedia. Evaluation results show the promise of this new question-guided search strategy.