@inproceedings{li-etal-2026-dawn,
title = "Dawn at {S}em{E}val-2026 Task 8: Structured Control Decomposition for Faithful Multi-Turn Retrieval-Augmented Generation",
author = "Li, Feiling and
Qi, Xiaoya and
Wang, Xunyue and
Chen, Pusheng and
Tang, Zhiwen and
Yang, Han",
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-1.363/",
pages = "2894--2902",
ISBN = "979-8-89176-414-9",
abstract = "Multi-turn Retrieval-Augmented Generation faces structural challenges that go beyond single-turn retrieval and fusion. Context-dependent queries, cross-turn evidence accumulation, and uncertain answerability jointly affect retrieval quality and generation reliability. We propose a structured control framework that formulates multi-turn RAG as a regulated reasoning process rather than a loosely coupled pipeline. The system first performs evidence and context structuring, extracting atomic facts strictly grounded in reference passages while reconstructing a self-contained query from dialogue history. It then conducts decision-conditioned generation, where explicit control signals regarding question intent, dialogue dependency, and answerability govern response feasibility, scope, and organization. By separating structural decision making from surface realization, the framework enforces consistent information flow across stages and reduces hallucination.Experiments on SemEval-2026 Task 8 show that our approach achieves strong faithfulness and stable overall performance, ranking 17/26 on Task B (generation, H=0.6333)."
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%0 Conference Proceedings
%T Dawn at SemEval-2026 Task 8: Structured Control Decomposition for Faithful Multi-Turn Retrieval-Augmented Generation
%A Li, Feiling
%A Qi, Xiaoya
%A Wang, Xunyue
%A Chen, Pusheng
%A Tang, Zhiwen
%A Yang, Han
%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 li-etal-2026-dawn
%X Multi-turn Retrieval-Augmented Generation faces structural challenges that go beyond single-turn retrieval and fusion. Context-dependent queries, cross-turn evidence accumulation, and uncertain answerability jointly affect retrieval quality and generation reliability. We propose a structured control framework that formulates multi-turn RAG as a regulated reasoning process rather than a loosely coupled pipeline. The system first performs evidence and context structuring, extracting atomic facts strictly grounded in reference passages while reconstructing a self-contained query from dialogue history. It then conducts decision-conditioned generation, where explicit control signals regarding question intent, dialogue dependency, and answerability govern response feasibility, scope, and organization. By separating structural decision making from surface realization, the framework enforces consistent information flow across stages and reduces hallucination.Experiments on SemEval-2026 Task 8 show that our approach achieves strong faithfulness and stable overall performance, ranking 17/26 on Task B (generation, H=0.6333).
%U https://aclanthology.org/2026.semeval-1.363/
%P 2894-2902
Markdown (Informal)
[Dawn at SemEval-2026 Task 8: Structured Control Decomposition for Faithful Multi-Turn Retrieval-Augmented Generation](https://aclanthology.org/2026.semeval-1.363/) (Li et al., SemEval 2026)
ACL