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Pseudo Relevance Feedback (PRF) is known to improve the effectiveness of bag-of-words retrievers.
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The simplest thing that can possibly work: pseudo-relevance feedback using text classification
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Roberta: A Robustly Optimized BERT Pretraining Approach
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A Pseudo-Relevance Feedback Framework Combining Relevance Matching and Semantic Matching for Information Retrieval
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RepBERT: Contextualized Text Embeddings for First-Stage Retrieval
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BERT-QE: Contextualized Query Expansion for Document Re-ranking. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings . 4718–4728
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Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 2862–2866
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Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
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How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
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RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
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Bert-based dense retrievers require interpolation with bm25 for effective passage retrieval. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval . 317–324
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Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
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