Multi-lingual Semantic Search for Domain-specific Applications: Adobe Photoshop and Illustrator Help Search

Jayant Kumar, Ashok Gupta, Zhaoyu Lu, Andrei Stefan, Tracy Holloway King

Abstract:

Search has become an integral part of Adobe products and users
rely on it to learn about tool usage, shortcuts, quick links, and
ways to add creative effects and to find assets such as backgrounds,
templates, and fonts. Within applications such as Photoshop and
Illustrator, users express domain-specific search intents via short
text queries. In this work, we leverage sentence-BERT models fine-tuned
on Adobe’s HelpX data to perform multi-lingual semantic
search on help and tutorial documents. We used behavioral data
(queries, clicks, and impressions) and additional annotated data
to train several BERT-based models for scoring query-document
pairs for semantic similarity. We benchmarked the keyword-based
production system against semantic search. Subsequent AB tests
demonstrate that this approach improves engagement for longer
queries while reducing null results significantly.