Our overall approach to re-ranking is as follows: from each of the top-k documents, extract five candidate passages we expect to be relevant to the query ( ...
Dec 31, 2020 · We explore three methods of passage extraction and find these approaches prove effective, performing comparably to the state-of-the-art while significantly ...
Faster BERT-based Re-ranking through Candidate Passage Extraction · Kyle Reed, Harish Tayyar Madabushi. Anthology ID: DBLP:conf/trec/ReedM20; Volume ...
Fingerprint. Dive into the research topics of 'Faster BERT-based re-ranking through Candidate Passage Extraction'. Together they form a unique fingerprint.
Jul 18, 2022 · The candidate set is then cut off to k documents and a more expensive re-ranking step is applied by using BERT-based Contextualized ranking ...
This work deals with the passage re-ranking step. BERT-based models have achieved high performance in passage re-ranking tasks. We find, however, that these ...
May 25, 2024 · This April 2020 paper discusses the integration of pre-trained deep language models, like BERT, into retrieval and ranking pipelines.
Aug 19, 2021 · BERT-based information retrieval models are expensive, in both time (query latency) and computational resources (energy, hardware cost), making ...
Missing: Candidate Extraction.
In passage ranking, candidate passages are ranked for a given question according to a relevance estimation. In this paper, we formulate the passage ranking ...
Jun 26, 2024 · In this paper we propose a novel approach for combining first-stage lexical retrieval models and Transformer-based re-rankers.