@inproceedings{manoj-kumar-etal-2026-curiosai,
title = "{C}urios{AI} at {S}em{E}val-2026 Task 8: Hybrid retrieval system with repeated sampling for generation",
author = "Manoj Kumar, Aiswariya and
Takushima, Hiroki and
Beppu, Fumika and
Shibata, Yuki and
Yamaga, Daichi and
Hori, Takayuki",
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-1.151/",
pages = "1106--1115",
ISBN = "979-8-89176-414-9",
abstract = "SemEval-2026 Task 8 (MTRAGEval) evaluates multi-turn Retrieval-Augmented Generation (RAG) under conversational challenges such as non-standalone turns, underspecification, and answerability detection. These conditions amplify retrieval and generation errors that standard single-turn RAG pipelines fail to address effectively. We present a robustness-oriented multi-turn RAG system combining contextual query rewriting, heterogeneous hybrid retrieval fused with Reciprocal Rank Fusion (RRF), domain-adaptive Low-Rank Adaptation (LoRA) reranking, and repeated sampling with metric-guided selection. On the official test set, our approach outperforms the organizers' baselines across all subtasks: Retrieval (nDCG@5: 0.5396 vs. 0.4795), Generation (0.7571 vs. 0.6390), and RAG (0.5486 vs. 0.5366). Our system ranks 5th in Subtask A, 5th in Subtask B, and 7th in Subtask C on the official leaderboard. These results demonstrate that calibrated hybrid retrieval combined with robust generation selection is effective for multi-turn RAG."
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<abstract>SemEval-2026 Task 8 (MTRAGEval) evaluates multi-turn Retrieval-Augmented Generation (RAG) under conversational challenges such as non-standalone turns, underspecification, and answerability detection. These conditions amplify retrieval and generation errors that standard single-turn RAG pipelines fail to address effectively. We present a robustness-oriented multi-turn RAG system combining contextual query rewriting, heterogeneous hybrid retrieval fused with Reciprocal Rank Fusion (RRF), domain-adaptive Low-Rank Adaptation (LoRA) reranking, and repeated sampling with metric-guided selection. On the official test set, our approach outperforms the organizers’ baselines across all subtasks: Retrieval (nDCG@5: 0.5396 vs. 0.4795), Generation (0.7571 vs. 0.6390), and RAG (0.5486 vs. 0.5366). Our system ranks 5th in Subtask A, 5th in Subtask B, and 7th in Subtask C on the official leaderboard. These results demonstrate that calibrated hybrid retrieval combined with robust generation selection is effective for multi-turn RAG.</abstract>
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%0 Conference Proceedings
%T CuriosAI at SemEval-2026 Task 8: Hybrid retrieval system with repeated sampling for generation
%A Manoj Kumar, Aiswariya
%A Takushima, Hiroki
%A Beppu, Fumika
%A Shibata, Yuki
%A Yamaga, Daichi
%A Hori, Takayuki
%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 manoj-kumar-etal-2026-curiosai
%X SemEval-2026 Task 8 (MTRAGEval) evaluates multi-turn Retrieval-Augmented Generation (RAG) under conversational challenges such as non-standalone turns, underspecification, and answerability detection. These conditions amplify retrieval and generation errors that standard single-turn RAG pipelines fail to address effectively. We present a robustness-oriented multi-turn RAG system combining contextual query rewriting, heterogeneous hybrid retrieval fused with Reciprocal Rank Fusion (RRF), domain-adaptive Low-Rank Adaptation (LoRA) reranking, and repeated sampling with metric-guided selection. On the official test set, our approach outperforms the organizers’ baselines across all subtasks: Retrieval (nDCG@5: 0.5396 vs. 0.4795), Generation (0.7571 vs. 0.6390), and RAG (0.5486 vs. 0.5366). Our system ranks 5th in Subtask A, 5th in Subtask B, and 7th in Subtask C on the official leaderboard. These results demonstrate that calibrated hybrid retrieval combined with robust generation selection is effective for multi-turn RAG.
%U https://aclanthology.org/2026.semeval-1.151/
%P 1106-1115
Markdown (Informal)
[CuriosAI at SemEval-2026 Task 8: Hybrid retrieval system with repeated sampling for generation](https://aclanthology.org/2026.semeval-1.151/) (Manoj Kumar et al., SemEval 2026)
ACL