Sparse, Dense, and Attentional Representations for Text Retrieval
Table 3: MRR@10 when reranking at different depth for the MS MARCO passage and document tasks.
['Retriever', 'Depth 10', 'Depth 20', 'Depth 50', 'Depth 100', 'Depth 200', 'Depth 500', 'Depth 1000']
Our focus is on improving the first efficient retrieval stage, and we compare to prior works in two settings: Retrieval, top part of the Table, where only first-stage efficient retrieval systems are used and Reranking, bottom part of the Table, where more expensive second-stage models are employed to re-rank candidates. The accuracy of the first-stage retrieval system is particularly important when the cost of reranking a large set of first-pass candidates is prohibitive. We see that, especially when a very small number, such as 10 or 20 candidates can be scored with expensive cross-attention models, our multi-vector ME-BERT and hybrid models achieve significant improvements compared to prior first-pass retrieval systems on both MS MARCO tasks.