Impact of Hybrid Dense-Sparse Retrieval Combinations on Recall-Precision Trade-offs in MSR-VTT Question Answering

Abstract

Abstract: We describe our system for SemEval-2026 Task 8 (MTRAGEval), participating in Task A (Retrieval) across four English-language domains. Our approach employs a three-stage pipeline: (1) query rewriting via a LoRA-fine-tuned Qwen 2.5 7B model that transforms context-dependent follow-up questions into standalone queries, (2) hybrid BM25 and dense retrieval combined through Reciprocal Rank Fusion, and (3) cross-encoder reranking with BGE-reranker-v2-m3. On the official test set, the system achieves nDCG@5 of 0.531, ranking 8th out of 38 participating systems and 10.7\% above the organizer baseline. D

Research Question

What is the impact of different hybrid dense-sparse retrieval combinations (e.g., DPR + BM25, ColBERT + SPLADE) on the recall-precision trade-off for question answering tasks in the MSR-VTT benchmark?

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