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Recent studies have shown that Dense Retrieval (DR) techniques cansignificantly improve the performance of first-stage retrieval in IR systems.Despite its empirical effectiveness, the application of DR is still limited. Incontrast to statistic retrieval models that rely on highly efficient invertedindex solutions, DR models build dense embeddings that are difficult to bepre-processed with most existing search indexing systems. To avoid theexpensive cost of brute-force search, the Approximate Nearest Neighbor (ANN)algorithm and corresponding indexes are widely applied to speed up theinference process of DR models. Unfortunately, while ANN can improve theefficiency of DR models, it usually comes with a significant price on retrievalperformance.  To solve this issue, we propose JTR, which stands for Joint optimization ofTRee-based index and query encoding. Specifically, we design a new unifiedcontrastive learning loss to train tree-based index and query encoder in anend-to-end manner. The tree-based negative sampling strategy is applied to makethe tree have the maximum heap property, which supports the effectiveness ofbeam search well. Moreover, we treat the cluster assignment as an optimizationproblem to update the tree-based index that allows overlapped clustering. Weevaluate JTR on numerous popular retrieval benchmarks. Experimental resultsshow that JTR achieves better retrieval performance while retaining high systemefficiency compared with widely-adopted baselines. It provides a potentialsolution to balance efficiency and effectiveness in neural retrieval systemdesigns.

Constructing Tree-based Index for Efficient and Effective Dense Retrieval