| Challenge: | Document relevance ranking is the task of ranking documents from a large collection using the query and the text of each document only. |
| Approach: | They propose to use convolutional n-gram matching to inject rich context-sensitive encodings into their models, inspired by PACRR's convolution-based ngram matching features. |
| Outcome: | The proposed models outperform baselines, DRMM, and PACRR on the BIOASQ and TREC ROBUST questions and document inputs. |
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| Challenge: | Existing approaches to document-to-document similarity ranking are limited to relatively short documents or lack similarity labels. |
| Approach: | They propose a self-supervised method for document similarity ranking that can be applied to documents of arbitrary length. |
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BERT-QE: Contextualized Query Expansion for Document Re-ranking (2020.findings-emnlp)
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Database-Augmented Query Representation for Information Retrieval (2025.emnlp-main)
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| Challenge: | Information retrieval models that aim to search for documents relevant to a query have shown multiple successes, but the query from the user is oftentimes short, which challenges the retrievers to correctly fetch relevant documents. |
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FAA: Fine-grained Attention Alignment for Cascade Document Ranking (2023.acl-long)
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| Challenge: | identifying relevant documents for Reasoning-intensive queries remains a challenge . large language models have shown strong performance in zero-shot document reranking . |
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Adaptive Document Retrieval for Deep Question Answering (D18-1)
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A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections (2021.acl-long)
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A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment (2025.findings-acl)
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Fusion-in-T5: Unifying Variant Signals for Simple and Effective Document Ranking with Attention Fusion (2024.lrec-main)
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| Challenge: | Current document ranking pipelines involve multiple ranking layers to integrate different information step-by-step. |
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