Joint question clustering and relevance prediction for open domain non-factoid question answering

Web searches are increasingly formulated as natural language questions, rather than keyword queries. Retrieving answers to such questions requires a degree of understanding of user expectations. An important step in this direction is to automatically infer the type of answer implied by the question, e.g., factoids, statements on a topic, instructions, reviews, etc. Answer Type taxonomies currently exist for factoid-style questions, but not for open-domain questions. Building taxonomies for non-factoid questions is a harder problem since these questions can come from a very broad semantic space. A few attempts have been made to develop taxonomies for non-factoid questions, but these tend to be too narrow or domain specific. In this paper, we address this problem by modeling the Answer Type as a latent variable that is learned in a data-driven fashion, allowing the model to be more adaptive to new domains and data sets. We propose approaches that detect the relevance of candidate answers to a user question by jointly 'clustering' questions according to the hidden variable, and modeling relevance conditioned on this hidden variable.