[图书][B] Pretrained transformers for text ranking: Bert and beyond
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …
response to a query. Although the most common formulation of text ranking is search …
Simplified data wrangling with ir_datasets
Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset
documentation is scattered across the Internet and once one obtains a copy of the data …
documentation is scattered across the Internet and once one obtains a copy of the data …
Generative relevance feedback with large language models
Current query expansion models use pseudo-relevance feedback to improve first-pass
retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of …
retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of …
Improving query representations for dense retrieval with pseudo relevance feedback
Dense retrieval systems conduct first-stage retrieval using embedded representations and
simple similarity metrics to match a query to documents. Its effectiveness depends on …
simple similarity metrics to match a query to documents. Its effectiveness depends on …
Pseudo relevance feedback with deep language models and dense retrievers: Successes and pitfalls
Pseudo Relevance Feedback (PRF) is known to improve the effectiveness of bag-of-words
retrievers. At the same time, deep language models have been shown to outperform …
retrievers. At the same time, deep language models have been shown to outperform …
Characterbert and self-teaching for improving the robustness of dense retrievers on queries with typos
Current dense retrievers are not robust to out-of-domain and outlier queries, ie their
effectiveness on these queries is much poorer than what one would expect. In this paper, we …
effectiveness on these queries is much poorer than what one would expect. In this paper, we …
Are we there yet? A decision framework for replacing term based retrieval with dense retrieval systems
Recently, several dense retrieval (DR) models have demonstrated competitive performance
to term-based retrieval that are ubiquitous in search systems. In contrast to term-based …
to term-based retrieval that are ubiquitous in search systems. In contrast to term-based …
Neural disentanglement of query difficulty and semantics
Researchers have shown that the retrieval effectiveness of queries may depend on other
factors in addition to the semantics of the query. In other words, several queries expressed …
factors in addition to the semantics of the query. In other words, several queries expressed …
Improving query representations for dense retrieval with pseudo relevance feedback: A reproducibility study
Abstract Pseudo-Relevance Feedback (PRF) utilises the relevance signals from the top-k
passages from the first round of retrieval to perform a second round of retrieval aiming to …
passages from the first round of retrieval to perform a second round of retrieval aiming to …
Unsupervised question clarity prediction through retrieved item coherency
Despite recent progress on conversational systems, they still do not perform smoothly when
faced with ambiguous requests. When questions are unclear, conversational systems …
faced with ambiguous requests. When questions are unclear, conversational systems …