Neural document ranking approaches, specifically transformer models, have achieved impressive gains in ranking performance. However, query processing using such over-parameterized models is both resource and time intensive. In this paper, we propose the Fast-Forward index -- a simple vector forward index that facilitates ranking documents using interpolation of lexical and semantic scores -- as a replacement for contextual rerankers and dense indexes based on nearest neighbor search. Fast-Forward indexes rely on efficient sparse models for retrieval and merely look up pre-computed dense transformer-based vector representations of documents and passages in constant time for fast CPU-based semantic similarity computation during query processi...
As information retrieval researchers, we not only develop algorithmic solutions to hard problems, bu...
In this article we propose Supervised Semantic Indexing (SSI) an algorithm that is trained on (query...
The task of Query Performance Prediction (QPP) in Information Retrieval (IR) involves predicting the...
Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transfor...
The availability of massive data and computing power allowing for effective data driven neural appro...
Neural ranking models use shallow or deep neural networks to rank search results in response to a qu...
Deep pretrained transformer networks are effective at various ranking tasks, such as question answer...
The recent availability of increasingly powerful hardware has caused a shift from traditional inform...
International audienceDocument indexing is a key component for efficient information retrieval (IR)....
Neural approaches that use pre-trained language models are effective at various ranking tasks, such ...
Dense retrieval, which describes the use of contextualised language models such as BERT to identify ...
Neural information retrieval systems typically use a cascading pipeline, in which a first-stage mode...
We incorporate the Latent Semantic Indexing (LSl) technique into a competition-based neural network ...
Text search engines return a set of k documents ranked by similarity to a query. Typically, document...
Learning to Rank (LtR) is an effective machine learning methodology for inducing high-quality docume...
As information retrieval researchers, we not only develop algorithmic solutions to hard problems, bu...
In this article we propose Supervised Semantic Indexing (SSI) an algorithm that is trained on (query...
The task of Query Performance Prediction (QPP) in Information Retrieval (IR) involves predicting the...
Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transfor...
The availability of massive data and computing power allowing for effective data driven neural appro...
Neural ranking models use shallow or deep neural networks to rank search results in response to a qu...
Deep pretrained transformer networks are effective at various ranking tasks, such as question answer...
The recent availability of increasingly powerful hardware has caused a shift from traditional inform...
International audienceDocument indexing is a key component for efficient information retrieval (IR)....
Neural approaches that use pre-trained language models are effective at various ranking tasks, such ...
Dense retrieval, which describes the use of contextualised language models such as BERT to identify ...
Neural information retrieval systems typically use a cascading pipeline, in which a first-stage mode...
We incorporate the Latent Semantic Indexing (LSl) technique into a competition-based neural network ...
Text search engines return a set of k documents ranked by similarity to a query. Typically, document...
Learning to Rank (LtR) is an effective machine learning methodology for inducing high-quality docume...
As information retrieval researchers, we not only develop algorithmic solutions to hard problems, bu...
In this article we propose Supervised Semantic Indexing (SSI) an algorithm that is trained on (query...
The task of Query Performance Prediction (QPP) in Information Retrieval (IR) involves predicting the...