Papers with ranking effectiveness
Dealing with Typos for BERT-based Passage Retrieval and Ranking (2021.emnlp-main)
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| Challenge: | Current approaches to passage retrieval and ranking rely on pre-trained deep language models that model the semantic matching between queries and passages. |
| Approach: | They propose a typos-aware training framework for DR and BERT to address this issue. |
| Outcome: | The proposed models respond and adapt to keyword typos occurring in queries, and significantly improve their retrieval and ranking effectiveness. |
Enhancing the Ranking Context of Dense Retrieval through Reciprocal Nearest Neighbors (2023.emnlp-main)
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| Challenge: | Sparse annotation poses persistent challenges to training dense retrieval models . despite potential future endeavors to extend annotation, issue of false negatives persists . |
| Approach: | They propose a method that smooths out the annotation of unlabeled relevant documents . they use reciprocal nearest neighbors to estimate relevance and rerank candidates . |
| Outcome: | The proposed method reduces the issue of false negatives in contrastive learning by reducing sparsity. |
Answering Narrative-Driven Recommendation Queries via a Retrieve–Rank Paradigm and the OCG-Agent (2025.emnlp-main)
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| Challenge: | Existing approaches to generate narrative-driven recommendation are based on large language models (LLMs) but the RAG paradigm is inherently ill-suited for such special queries. |
| Approach: | They propose a novel retrieve-rank paradigm that generatively retrieves structurally adaptive and semantically aligned candidates, ensuring both extensive candidate coverage and high-quality information. |
| Outcome: | The proposed paradigm outperforms the existing paradigm and the existing one under real-world scenarios. |
Discovering Biases in Information Retrieval Models Using Relevance Thesaurus as Global Explanation (2024.emnlp-main)
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| Challenge: | Currently, local explanations are not effective in predicting the model’s behavior on unseen texts. |
| Approach: | They propose a method to build a relevance thesaurus containing semantically relevant query term and document term pairs which can augment BM25 scoring functions to better approximate the neural model’s predictions. |
| Outcome: | The proposed method can augment BM25 scoring functions to better approximate the neural relevance model’s predictions. |