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We present an approach to ranking with dense representations that appliesknowledge distillation to improve the recently proposed late-interactionColBERT model. Specifically, we distill the knowledge from ColBERT's expressiveMaxSim operator for computing relevance scores into a simple dot product, thusenabling single-step ANN search. Our key insight is that during distillation,tight coupling between the teacher model and the student model enables moreflexible distillation strategies and yields better learned representations. Weempirically show that our approach improves query latency and greatly reducesthe onerous storage requirements of ColBERT, while only making modestsacrifices in terms of effectiveness. By combining our dense representationswith sparse representations derived from document expansion, we are able toapproach the effectiveness of a standard cross-encoder reranker using BERT thatis orders of magnitude slower.

Distilling Dense Representations for Ranking using Tightly-Coupled Teachers