@inproceedings{ferreira-etal-2026-akcit,
title = "{AKCIT}-{UFG} at {S}em{E}val-2026 Task 8: Structured Chunking and Optimized Query Reformulation for Efficient Multi-Turn Retrieval",
author = "Ferreira, David and
Ramos, Wilson and
Ribeiro, Priscila and
Passinato, Emanuel and
Fernandes, Diogo and
Filho, Arlindo",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.395/",
pages = "3149--3155",
ISBN = "979-8-89176-414-9",
abstract = "This submission investigates efficient multi-turn retrieval under constrained computational settings. We analyze how passage granularity and conversational query rewriting affect retrieval effectiveness across four benchmark domains. Using compact, locally deployable components, we show that smaller passage segmentation improves early-rank performance and that lightweight keyword-oriented query reformulation substantially enhances dense retrieval quality.Importantly, we observe that rewriting interacts differently with encoder backbones: some compact models benefit significantly from increased query specificity, while others degrade, indicating sensitivity to rewrite-induced distribution shifts. Our findings demonstrate that competitive multi-turn retrieval does not require large proprietary models, but can emerge from principled structural and preprocessing design choices. The results highlight the importance of aligning chunking strategy, rewriting policy, and encoder characteristics in resource-efficient MT-RAG systems."
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<abstract>This submission investigates efficient multi-turn retrieval under constrained computational settings. We analyze how passage granularity and conversational query rewriting affect retrieval effectiveness across four benchmark domains. Using compact, locally deployable components, we show that smaller passage segmentation improves early-rank performance and that lightweight keyword-oriented query reformulation substantially enhances dense retrieval quality.Importantly, we observe that rewriting interacts differently with encoder backbones: some compact models benefit significantly from increased query specificity, while others degrade, indicating sensitivity to rewrite-induced distribution shifts. Our findings demonstrate that competitive multi-turn retrieval does not require large proprietary models, but can emerge from principled structural and preprocessing design choices. The results highlight the importance of aligning chunking strategy, rewriting policy, and encoder characteristics in resource-efficient MT-RAG systems.</abstract>
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%0 Conference Proceedings
%T AKCIT-UFG at SemEval-2026 Task 8: Structured Chunking and Optimized Query Reformulation for Efficient Multi-Turn Retrieval
%A Ferreira, David
%A Ramos, Wilson
%A Ribeiro, Priscila
%A Passinato, Emanuel
%A Fernandes, Diogo
%A Filho, Arlindo
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F ferreira-etal-2026-akcit
%X This submission investigates efficient multi-turn retrieval under constrained computational settings. We analyze how passage granularity and conversational query rewriting affect retrieval effectiveness across four benchmark domains. Using compact, locally deployable components, we show that smaller passage segmentation improves early-rank performance and that lightweight keyword-oriented query reformulation substantially enhances dense retrieval quality.Importantly, we observe that rewriting interacts differently with encoder backbones: some compact models benefit significantly from increased query specificity, while others degrade, indicating sensitivity to rewrite-induced distribution shifts. Our findings demonstrate that competitive multi-turn retrieval does not require large proprietary models, but can emerge from principled structural and preprocessing design choices. The results highlight the importance of aligning chunking strategy, rewriting policy, and encoder characteristics in resource-efficient MT-RAG systems.
%U https://aclanthology.org/2026.semeval-1.395/
%P 3149-3155
Markdown (Informal)
[AKCIT-UFG at SemEval-2026 Task 8: Structured Chunking and Optimized Query Reformulation for Efficient Multi-Turn Retrieval](https://aclanthology.org/2026.semeval-1.395/) (Ferreira et al., SemEval 2026)
ACL