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Hybrid Retrieval and Multi-stage Text Ranking Solution at TREC 2022 Deep Learning Track

arXiv 2308.12039 ↗First posted 23 Aug 2023cs.IR cs.CLcited by 0

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Abstract (arXiv)

Large-scale text retrieval technology has been widely used in various practical business scenarios. This paper presents our systems for the TREC 2022 Deep Learning Track. We explain the hybrid text retrieval and multi-stage text ranking method adopted in our solution. The retrieval stage combined the two structures of traditional sparse retrieval and neural dense retrieval. In the ranking stage, in addition to the full interaction-based ranking model built on large pre-trained language model, we also proposes a lightweight sub-ranking module to further enhance the final text ranking performance. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 1st and 4th rank on the test set of passage ranking and document ranking respectively.

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References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  4. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  5. 2021Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrievalcited by 203
  6. 2021Condenser: a Pre-training Architecture for Dense Retrievalcited by 162
  7. 2021SPLADE v2: Sparse Lexical and Expansion Model for Information Retrievalcited by 127
  8. 2022ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrievalcited by 41
  9. 2022HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Rerankingcited by 20
  10. 2022Retrieval Oriented Masking Pre-training Language Model for Dense Passage Retrievalcited by 4

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