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HyReC: Exploring Hybrid-based Retriever for Chinese

arXiv 2506.21913 ↗First posted 27 Jun 2025cs.IR cs.CLcited by 0

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Abstract (arXiv)

Hybrid-based retrieval methods, which unify dense-vector and lexicon-based retrieval, have garnered considerable attention in the industry due to performance enhancement. However, despite their promising results, the application of these hybrid paradigms in Chinese retrieval contexts has remained largely underexplored. In this paper, we introduce HyReC, an innovative end-to-end optimization method tailored specifically for hybrid-based retrieval in Chinese. HyReC enhances performance by integrating the semantic union of terms into the representation model. Additionally, it features the Global-Local-Aware Encoder (GLAE) to promote consistent semantic sharing between lexicon-based and dense retrieval while minimizing the interference between them. To further refine alignment, we incorporate a Normalization Module (NM) that fosters mutual benefits between the retrieval approaches. Finally, we evaluate HyReC on the C-MTEB retrieval benchmark to demonstrate its effectiveness.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 28 references that are in the map. First posted on arXiv 27 Jun 2025.

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17 of 28 references.

References · 17

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5 more
  1. 2019Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrievalcited by 65
  2. 2020SparTerm: Learning Term-based Sparse Representation for Fast Text Retrievalcited by 45
  3. 2024PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrievalcited by 30
  4. 2021Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansioncited by 20
  5. 2022UnifieR: A Unified Retriever for Large-Scale Retrievalcited by 19

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