Abstract
Carlos Lassance, Hervé Déjean, Simon Lupart, Stéphane Clinchant, Nicola Tonellotto |
46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23), Taipei, Taiwan, 23-27 July, 2023 |
arXiv.org |
Abstract
Sparse neural retrievers, such as DeepImpact, uniCOIL and SPLADE, have been introduced recently as an efficient and effective way to perform retrieval with inverted indexes. They aim to learn term importance and, in some cases, document expansions, to provide a more effective document ranking compared to traditional bag-ofwords retrieval models such as BM25. However, these sparse neural retrievers have been shown to increase the computational costs and latency of query processing compared to their classical counterparts. To mitigate this, we apply a well-known family of techniques for boosting the efficiency of query processing over inverted indexes: static pruning. We experiment with three static pruning strategies, namely document-centric, term-centric and agnostic pruning, and we assess, over diverse datasets, that these techniques still work with sparse neural retrievers. In particular, static pruning achieves 2× speedup with negligible effectiveness loss (≤ 2% drop) and, depending on the use case, even 4× speedup with minimal impact on the effectiveness (≤ 8% drop). Moreover, we show that neural rerankers are robust to candidates from statically pruned indexes.
Details on the gender equality index score 2023 (related to year 2022) for NAVER France of 81/100.
NAVER France targets are as follows:
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2023 au titre des données 2022 : 81/100
Détail des indicateurs :
Les objectifs de progression de NAVER France sont :
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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