@inproceedings{novais-etal-2026-optimizing,
title = "Optimizing Efficiency in Multi-Stage Semantic Re-ranking Architectures",
author = "Novais, Artur M. A. and
Moreira, Anna P. V. L. B. and
Almeida, Maria C. X. de and
Presa, Jo{\~a}o P. C. and
Federson, Fernando M. and
Oliveira, S{\'a}vio S. T. de",
editor = "Souza, Marlo and
de-Dios-Flores, Iria and
Santos, Diana and
Freitas, Larissa and
Souza, Jackson Wilke da Cruz and
Ribeiro, Eug{\'e}nio",
booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-1.55/",
pages = "562--571",
ISBN = "979-8-89176-387-6",
abstract = "Semantic re-ranking architectures based on cross-encoders are essential for high-precision Information Retrieval (IR) in the legal domain, but they face a dilemma: their high computational latency renders large-scale applications challenging, particularly in resource-constrained environments. Traditional single-stage approaches force a choice between computational efficiency and ranking quality. This work presents an empirical evaluation of established cascade re-ranking architectures to optimize this balance through the adaptive application of off-the-shelf models of increasing complexity over progressively smaller sets of candidates. We validated the architecture on a corpus of 300,000 legal documents in Portuguese from the Court of Accounts of the State of Goi{\'a}s (TCE-GO). Experiments demonstrate a 60.3{\%} reduction in latency (from 11.75s to 4.66s per query) compared to the most precise single-stage baseline, with a marginal degradation of only 2 p.p. in R@avg and 0.0224 in MRR@avg. The results validate the semantic funnel as a computationally viable solution for semantic document-to-document search within the specific context of the TCE-GO repository, establishing a baseline for future transferability studies in broader Portuguese legal contexts."
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%0 Conference Proceedings
%T Optimizing Efficiency in Multi-Stage Semantic Re-ranking Architectures
%A Novais, Artur M. A.
%A Moreira, Anna P. V. L. B.
%A Almeida, Maria C. X. de
%A Presa, João P. C.
%A Federson, Fernando M.
%A Oliveira, Sávio S. T. de
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F novais-etal-2026-optimizing
%X Semantic re-ranking architectures based on cross-encoders are essential for high-precision Information Retrieval (IR) in the legal domain, but they face a dilemma: their high computational latency renders large-scale applications challenging, particularly in resource-constrained environments. Traditional single-stage approaches force a choice between computational efficiency and ranking quality. This work presents an empirical evaluation of established cascade re-ranking architectures to optimize this balance through the adaptive application of off-the-shelf models of increasing complexity over progressively smaller sets of candidates. We validated the architecture on a corpus of 300,000 legal documents in Portuguese from the Court of Accounts of the State of Goiás (TCE-GO). Experiments demonstrate a 60.3% reduction in latency (from 11.75s to 4.66s per query) compared to the most precise single-stage baseline, with a marginal degradation of only 2 p.p. in R@avg and 0.0224 in MRR@avg. The results validate the semantic funnel as a computationally viable solution for semantic document-to-document search within the specific context of the TCE-GO repository, establishing a baseline for future transferability studies in broader Portuguese legal contexts.
%U https://aclanthology.org/2026.propor-1.55/
%P 562-571
Markdown (Informal)
[Optimizing Efficiency in Multi-Stage Semantic Re-ranking Architectures](https://aclanthology.org/2026.propor-1.55/) (Novais et al., PROPOR 2026)
ACL
- Artur M. A. Novais, Anna P. V. L. B. Moreira, Maria C. X. de Almeida, João P. C. Presa, Fernando M. Federson, and Sávio S. T. de Oliveira. 2026. Optimizing Efficiency in Multi-Stage Semantic Re-ranking Architectures. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 562–571, Salvador, Brazil. Association for Computational Linguistics.