MgSFR: Multi-grained Semantic Fusion Retrieval for Multi-hop Reading Comprehension

Multi-hop Reading Comprehension RC has become a critical task in natural language Processing NLP , requiring models to perform multiple reasoning steps and aggregate dispersed clues across multiple paragraphs to answer complex questions. Unlike single-hop RC, multi-hop RC aims to bridge information across diverse contexts and provide interpretable supporting facts, making it closer to human-like reasoning. To address these challenges, we propose a novel approach, Multi-grained Semantic Fusion Retrieval MgSFR , which integrates semantic information from multiple granularities word, phrase, sentence, and document . This fusion enhances the semantic relevance between questions and paragraphs, improving retrieval accuracy and efficiency. Additionally, MgSFR proposes a fine-grained semantic interaction mechanism that computes semantic similarity between different granularities, further boosting the model's performance. To complete the multi-hop RC pipeline, we introduce a multi-task reader that leverages this semantic fusion to enhance the model's reasoning capabilities. Experimental results on the HotpotQA benchmark dataset demonstrate that MgSFR significantly outperforms existing retrieval methods and provides high-quality context for multi-hop reasoning. Additionally, MgSFR achieves competitive performance compared to current state-of-the-art models in multi-hop reasoning tasks, validating its effectiveness in complex multi-hop tasks.