Sparse, Dense, and Attentional Representations for Text Retrieval
Table 2: Results on MS MARCO-Passage (MS-Passage), MS MARCO-Document (MS-Doc) and TREC-CAR datasets. We report MRR@10 and MAP@1000 to align with prior work. For the MS MARCO datasets, results are on the development set; the TREC-CAR results are on the test set.
['[EMPTY]', 'Model', 'MS-Passage MRR', 'MS-Doc MRR', 'TRECCAR MRR', 'TRECCAR MAP']
The state of the art prior work follows the two-stage retrieval and reranking approach, where an efficient first-stage system retrieves a (usually large) list of candidates from the document collection, and a second stage more expensive model such as cross-attention BERT reranks the candidates. The multi-vector model brings largest improvement on the dataset containing the longest documents (MS MARCO document), and sparse-dense hybrid model brings improvements over dense-only models on all datasets. The improvement from the hybrid model is smallest on the collection with shortest passages (MS MARCO passage).