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Found 7 papers, 7 papers with code
DeeperImpact: Optimizing Sparse Learned Index Structures
A lot of recent work has focused on sparse learned indexes that use deep neural architectures to significantly improve retrieval quality while keeping the efficiency benefits of the inverted index.
Faster Learned Sparse Retrieval with Block-Max Pruning
Learned sparse retrieval systems aim to combine the effectiveness of contextualized language models with the scalability of conventional data structures such as inverted indexes.
Improved Learned Sparse Retrieval with Corpus-Specific Vocabularies
We explore leveraging corpus-specific vocabularies that improve both efficiency and effectiveness of learned sparse retrieval systems.
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.
Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format
There exists a natural tension between encouraging a diverse ecosystem of open-source search engines and supporting fair, replicable comparisons across those systems.
Debunking Fake News One Feature at a Time
Identifying the stance of a news article body with respect to a certain headline is the first step to automated fake news detection.
