| Challenge: | Existing test collections provide only document-level relevance judgments, and documents exceed the length that BERT was designed to handle. |
| Approach: | They propose to aggregate sentence-level evidence to rank news articles using BERT . they also leverage passage-level relevance judgments available in other domains to fine-tune BERT models that capture cross-domain notions of relevance. |
| Outcome: | The proposed model aggregates sentence-level evidence to rank documents on three standard test collections. |
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| Challenge: | Existing methods to expand query use pseudo relevance feedback (PRF) but they are under-equipped to evaluate the relevance of information pieces used for expansion. |
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Cross-Lingual Training of Neural Models for Document Ranking (2020.findings-emnlp)
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| Challenge: | a recent study shows that multi-lingual BERT models can be used for document ranking in non-English languages . a blog post by Google suggests that the company is exploring this approach to improve web search across a number of languages. |
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BERT Has More to Offer: BERT Layers Combination Yields Better Sentence Embeddings (2023.findings-emnlp)
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DocInfer: Document-level Natural Language Inference using Optimal Evidence Selection (2022.emnlp-main)
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Applying BERT to Document Retrieval with Birch (D19-3)
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| Challenge: | Existing methods for large-scale query-document retrieval are expensive and require sparse handcrafted features. |
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Document-Level Neural Machine Translation Using BERT as Context Encoder (2020.aacl-srw)
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BERT meets Cranfield: Uncovering the Properties of Full Ranking on Fully Labeled Data (2021.eacl-srw)
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Retrieving Support to Rank Answers in Open-Domain Question Answering (2025.emnlp-main)
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