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 …

In this paper we propose a novel approach for combining first-stage lexical retrieval models
and Transformer-based re-rankers: we inject the relevance score of the lexical model as a …

Heavily pre-trained transformers for language modeling, such as BERT, have shown to be
remarkably effective for Information Retrieval (IR) tasks, typically applied to re-rank the …

Explainable information retrieval is an emerging research area aiming to make transparent
and trustworthy information retrieval systems. Given the increasing use of complex machine …

Query performance prediction (QPP) methods, which aim to predict the performance of a
query, often rely on evidences in the form of different characteristic patterns in the …

Ranking models are the main components of information retrieval systems. Several
approaches to ranking are based on traditional machine learning algorithms using a set of …

This paper studies a category of visual question answering tasks, in which accessing
external knowledge is necessary for answering the questions. This category is called …

When ranking a list of documents relative to a given query, the vocabulary mismatches could
compromise the performance, as a result of the different language used in the queries and …

Job‐resume matching (JRM) is the core of online recruitment services for predicting the
matching degree between a job post and a resume. Most of the existing methods for JRM …

In recent years, large pre-trained transformers have led to substantial gains in performance
over traditional retrieval models and feedback approaches. However, these results are …