Improving Document Retrieval Coherence for Semantically Equivalent Queries

• URL: http://arxiv.org/abs/2508.07975v1
• Date: Mon, 11 Aug 2025 13:34:59 GMT
• Title: Improving Document Retrieval Coherence for Semantically Equivalent Queries
• Authors: Stefano Campese, Alessandro Moschitti, Ivano Lauriola,
• Abstract summary: We propose a variation of the Multi-Negative Ranking loss for training DR that improves the coherence of models in retrieving the same documents.<n>The loss penalizes discrepancies between the top-k ranked documents retrieved for diverse but semantic equivalent queries.
• Score: 63.97649988164166
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dense Retrieval (DR) models have proven to be effective for Document Retrieval and Information Grounding tasks. Usually, these models are trained and optimized for improving the relevance of top-ranked documents for a given query. Previous work has shown that popular DR models are sensitive to the query and document lexicon: small variations of it may lead to a significant difference in the set of retrieved documents. In this paper, we propose a variation of the Multi-Negative Ranking loss for training DR that improves the coherence of models in retrieving the same documents with respect to semantically similar queries. The loss penalizes discrepancies between the top-k ranked documents retrieved for diverse but semantic equivalent queries. We conducted extensive experiments on various datasets, MS-MARCO, Natural Questions, BEIR, and TREC DL 19/20. The results show that (i) models optimizes by our loss are subject to lower sensitivity, and, (ii) interestingly, higher accuracy.

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