@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://preview.aclanthology.org/ingest-acl-workshops/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."
}