CEDR
Sean MacAvaney, Andrew Yates, Arman Cohan, Nazli Goharian
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several ex-sting neural ranking archi ...
tectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models.
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Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models.
Implementation Evidence Summary
Georgetown-IR-Lab/cedr is the closest maintained adjacent implementation (Strong overlap with paper title keywords). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 156 GitHub stars.
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Evidence disclosure
Evidence graph: 3 refs, 3 links.
Utility signals: depth 65/100, grounding 75/100, status medium.
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- Georgetown-IR-Lab/cedrAdjacentConfidence: LowStars: 156
Strong overlap with paper title keywords
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Research context
235
Citations
11
References
Tasks
Computer science, Context (archaeology), Joint (building), Artificial neural network, Term (time), Natural language, Language understanding, Key (lock)
Methods
Ranking (information retrieval), Language model, Context model
Domains
Artificial intelligence, Natural language processing, Machine learning
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