Tempura: Query Analysis with Structural Templates

要旨

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.

キーワード
Natural Language Processing
Error Analysis
Query Analysis
著者
Tongshuang Wu
University of Washington & Apple Inc., Seattle, WA, USA
Kanit Wongsuphasawat
Apple Inc., Seattle, WA, USA
Donghao Ren
Apple Inc., Seattle, WA, USA
Kayur Patel
Apple Inc., Seattle, WA, USA
Chris DuBois
Apple Inc., Seattle, WA, USA
DOI

10.1145/3313831.3376451

動画

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems

セッション: Speech & language

Paper session
Paper
312 NI'IHAU
2020-04-29 20:00:00
2020-04-29 21:15:00
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