Sifei at SemEval-2026 Task 8: Hybrid Retrieval and Query Rewriting for Multi-Turn RAG
For participants in the SemEval-2026 shared task, this work provides a competitive, training-free retrieval approach that outperforms the baseline, though it is an incremental improvement over existing methods.
The paper proposes a training-free hybrid retrieval pipeline combining dense and sparse retrieval with query rewriting and reranking for multi-turn RAG, achieving 0.5453 nDCG@5 (3rd out of 38 teams) on SemEval-2026 Task A and 0.5312 harmonic mean (15th out of 29) on Task C.
Multi-turn retrieval-augmented generation (RAG) is challenging due to evolving user intent, conversational noise, and strict context limits. We propose a training-free hybrid retrieval pipeline for SemEval-2026 Task 8 that combines dense and sparse retrieval with controlled query rewriting and cross-encoder reranking. On the official test set of Task A, our system achieves 0.5453 nDCG@5, ranking third among 38 teams and outperforming the strongest baseline score of 0.4795. For Task C, we reuse the documents retrieved for Task A and apply a lightweight generation pipeline guided by the official prompt, achieving 0.5312 as the harmonic mean of relevance and faithfulness and ranking 15th among 29 teams. All retrieval components are open-source, while query rewriting and answer generation rely on LLM APIs.