Search Results for author:
Found 2 papers, 2 papers with code
Faster Learned Sparse Retrieval with Guided Traversal
Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25.
Learning Passage Impacts for Inverted Indexes
Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-rank this set using contextualized language models such as BERT.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
