Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the application of DR is still limited. In contrast to statistic retrieval models that rely on highly efficient inverted index solutions, DR models build dense embeddings that are difficult to be pre-processed with most existing search indexing systems. To avoid the expensive cost of brute-force search, the Approximate Nearest Neighbor (ANN) algorithm and corresponding indexes are widely applied to speed up the inference process of DR models. Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance. To solve this issue, we propose JTR, which stands for Joint optimization of TRee-based index and query encoding. Specifically, we design a new unified contrastive learning loss to train tree-based index and query encoder in an end-to-end manner. The tree-based negative sampling strategy is applied to make the tree have the maximum heap property, which supports the effectiveness of beam search well. Moreover, we treat the cluster assignment as an optimization problem to update the tree-based index that allows overlapped clustering. We evaluate JTR on numerous popular retrieval benchmarks. Experimental results show that JTR achieves better retrieval performance while retaining high system efficiency compared with widely-adopted baselines. It provides a potential solution to balance efficiency and effectiveness in neural retrieval system designs.

翻译：最近的研究表明，密集检索技术能显著提升信息检索系统中第一阶段检索的性能。尽管具有经验有效性，密集检索的应用仍受到限制。与依赖高效倒排索引解决方案的统计检索模型不同，密集检索模型生成的密集嵌入难以被现有大多数搜索索引系统预处理。为避免暴力搜索的高昂成本，近似最近邻算法及其对应索引被广泛用于加速密集检索模型的推理过程。然而，虽然近似最近邻能提升密集检索模型的效率，但通常会导致检索性能显著下降。为解决此问题，我们提出JTR（基于树的索引与查询编码的联合优化）。具体而言，我们设计了一种新的统一对比学习损失函数，以端到端方式训练基于树的索引和查询编码器。采用基于树的负采样策略，使树具备最大堆性质，从而充分支持波束搜索的有效性。此外，我们将聚类分配视为优化问题，以更新允许重叠聚类的树索引。我们在多个主流检索基准上评估JTR。实验结果表明，与广泛采用的基线方法相比，JTR在保持高系统效率的同时实现了更优的检索性能，为神经检索系统中平衡效率与有效性提供了潜在解决方案。