Blending Learning to Rank and Dense Representations for Efficient and Effective Cascades

• URL: http://arxiv.org/abs/2510.16393v1
• Date: Sat, 18 Oct 2025 08:16:48 GMT
• Title: Blending Learning to Rank and Dense Representations for Efficient and Effective Cascades
• Authors: Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Salvatore Trani,
• Abstract summary: We investigate the exploitation of both lexical and neural relevance signals for ad-hoc passage retrieval.<n>We employ a pipelined architecture where a dense neural retriever serves as the first stage and performs a nearest-neighbor search.<n>We achieve a boost in nDCG@10 of up to 11% with an increase in average query latency of only 4.3%.
• Score: 18.081997057477178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the exploitation of both lexical and neural relevance signals for ad-hoc passage retrieval. Our exploration involves a large-scale training dataset in which dense neural representations of MS-MARCO queries and passages are complemented and integrated with 253 hand-crafted lexical features extracted from the same corpus. Blending of the relevance signals from the two different groups of features is learned by a classical Learning-to-Rank (LTR) model based on a forest of decision trees. To evaluate our solution, we employ a pipelined architecture where a dense neural retriever serves as the first stage and performs a nearest-neighbor search over the neural representations of the documents. Our LTR model acts instead as the second stage that re-ranks the set of candidates retrieved by the first stage to enhance effectiveness. The results of reproducible experiments conducted with state-of-the-art dense retrievers on publicly available resources show that the proposed solution significantly enhances the end-to-end ranking performance while relatively minimally impacting efficiency. Specifically, we achieve a boost in nDCG@10 of up to 11% with an increase in average query latency of only 4.3%. This confirms the advantage of seamlessly combining two distinct families of signals that mutually contribute to retrieval effectiveness.

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