Automatic Identification and Contextual Reformulation of Implicit System-Related Queries

Web search functionality is increasingly integrated into operating systems, software applications, and other interactive environments that extend beyond the traditional web browser. In particular, intelligent virtual assistants (e.g., Microsoft Cortana or Apple Siri) often “fall-back” to generic web search in cases where utterances fall outside the set of scenarios known to the agent. In this paper we analyze a 3 month log of web search queries posed via the Cortana virtual assistant. We report that, in this environment, users frequently ask questions that implicitly pertain to the systems or devices from which they are searching (e.g., asking: [how do I take a screenshot]). Unfortunately, accurately answering these implicit system queries poses significant challenges to general web search engines, due in part to the lack of available context. We show that such queries: (1) can be detected with high precision, (2) are common, and (3) can be automatically reformulated to substantially improve retrieval performance in these fall-through scenarios.