用混合检索与生成式查询优化，提升古兰经多节段文本召回准确率。
Hybrid Neural Retrieval with Generative Query Refinement for Quranic Passage Retrieval
- 融合稠密与稀疏检索，再用跨模态编码器重排序。
- 在扩展版古兰经数据集上达Recall@10=0.7024，MAP@10=0.4947。
- 适合需要高可靠性与上下文理解的宗教文本检索场景。
古兰经文本检索因语言复杂性及日常查询所用现代标准阿拉伯语（MSA）与古兰经古典阿拉伯语（CA）间的语义鸿沟而具挑战性，传统方法效果受限。本文提出四阶段神经架构以提升多节段检索性能并过滤无答案查询。方法结合AraColBERT稠密索引与BM25稀疏检索进行候选召回，再通过CAMeLBERTmix跨编码器实现语义重排序，引入置信度门控机制过滤零答案查询，并使用AraT5模型进行多节段聚合。系统在扩展版Quran QA 2022数据集上评估，性能优于基线模型，取得Recall@10=0.7024、MAP@10=0.4947；虽在绝对首位精度（MRR=0.5807）上略逊于高度优化的单模型，但整体更具全面性、可靠性与上下文感知能力。
原文摘要 · Abstract (English)
Quranic Passage Retrieval (PR) could be a challenging task due to the linguistic complexity and the semantic gap between the Modern Standard Arabic (MSA) used in daily queries and the Classical Arabic (CA) of the Holy Quran. These factors hinder conventional retrieval methods. To handle these limitations and improve multi-verse retrieval and filter the zero-answer queries, this paper proposes a four-phase neural architecture designed to enhance retrieval accuracy and contextual understanding. The methodology combines hybrid candidate retrieval using AraColBERT dense indexing and BM25 sparse retrieval, followed by semantic reranking with a CAMeLBERTmix cross-encoder. A confidence gating mechanism is then applied to filter zero-answer queries, and an AraT5-based refinement module for multi-verse aggregation. The system is evaluated on an expanded version of the Quran QA 2022 dataset. Results show improved performance compared to the baseline models, achieving a Recall@10 of 0.7024 and a Mean Average Precision (MAP@10) of 0.4947. While the system exhibits a marginal tradeoff in absolute top-rank precision (MRR = 0.5807) compared to heavily optimised single models, the proposed architecture provides a substantially more comprehensive, reliable, and context aware solution for multi-verse Quranic passage retrieval.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。