Watch: Search Query Quality Classifier
A search query classifier using ALBERT architecture to identify well-formed queries with 80% accuracy, improving upon Google's LSTM-based model by 10%.
Transcript
Building on research from Google AI, a new search query classifier is helping make sense of how people search online. This model is designed to identify well-formed search queries, building directly on the work of Manaal Faruqui and Dipanjan Das.
By using a modern ALBERT architecture instead of older Long Short-Term Memory, or L-S-T-M, networks, the model achieves an eighty percent accuracy rate. That is a ten percent improvement over Google's original classifier.
Because of this high accuracy, the model is already fully deployed in Dejan AI's query processing pipeline. Its main job is to flag ambiguous queries retrieved from the Google Search Console, helping the system find better candidates for query expansion.
To train the model, developers used Google’s original dataset alongside data provided by Owayo. It is a production-ready solution that represents a significant step forward in understanding the quality of search queries.