Tempura: Query Analysis with Structural Templates
In collaboration with University of Washington
authors Tongshuang Wu, Kanit Wongsuphasawat, Donghao Ren, Kayur Patel, Chris DuBois
Analyzing queries from search engines and intelligent assistants is difficult. A key challenge is organizing queries into interpretable, context-preserving, representative, and flexible groups. We present structural templates, abstract queries that replace tokens with their linguistic feature forms, as a query grouping method. The templates allow analysts to create query groups with structural similarity at different granularities. We introduce Tempura, an interactive tool that lets analysts explore a query dataset with structural templates. Tempura summarizes a query dataset by selecting a representative subset of templates to show the query distribution. The tool also helps analysts navigate the template space by suggesting related templates likely to yield further explorations. Our user study shows that Tempura helps analysts examine the distribution of a query dataset, find labeling errors, and discover model error patterns and outliers.
Apple had three papers accepted at the conference of Human-Computer Interaction (CHI), the premier international conference on interactive technology, in April 2020. Researchers from across the world gather at CHI to discuss, research, and design new ways for people to interact using technology. Although the conference was not held this year, you can read the accepted papers below.