Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation

Understand

Retrieval and ranking models are the backbone of many applications such as web search, open domain QA, or text-based recommender systems.

• The latency of neural ranking models at query time is largely dependent on the architecture and deliberate choices by their designers to trade-off effectiveness for higher efficiency.
• This focus on low query latency of a rising number of efficient ranking architectures make them feasible for production deployment.
• In machine learning an increasingly common approach to close the effectiveness gap of more efficient models is to apply knowledge distillation from a large teacher model to a smaller student model.