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 …

This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the
task of document ranking. Our approach is fundamentally different from a commonly …

Recently, neural models pretrained on a language modeling task, such as ELMo (Peters et
al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved …

The advent of deep neural networks pre-trained via language modeling tasks has spurred a
number of successful applications in natural language processing. This work explores one …

Recent years have witnessed a substantial increase in the use of deep learning to solve
various natural language processing (NLP) problems. Early deep learning models were …

Although considerable attention has been given to neural ranking architectures recently, far
less attention has been paid to the term representations that are used as input to these …

Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …

Pre-trained transformer models, such as BERT and T5, have shown to be highly effective at
ad hoc passage and document ranking. Due to the inherent sequence length limits of these …

When experiencing an information need, users want to engage with a domain expert, but
often turn to an information retrieval system, such as a search engine, instead. Classical …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …