Tempura: Query Analysis with Structural Templates

By Tongshuang Sherry Wu, Kanit Wongsuphasawat, Donghao Ren. 2020

20 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Management Science and Operations Research

In Natural Language Processing

Citations in the corpus

Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models (2021, methodology): “Similar to Wu et al. (2020), the process of finding representative perturbation patterns takes two steps: Extract template.”