Internet publications are staying atop of local and international events, generating hundreds,
sometimes thousands of news articles per day, making it difficult for readers to navigate this …

Although representational retrieval models based on Transformers have been able to make
major advances in the past few years, and despite the widely accepted conventions and …

This paper describes our experiments in using document embeddings to provide
background links to news articles. This work was done as part of the recent TREC 2020 …

This report describes the training dataset creation and recipe behind the family of\texttt
{arctic-embed} text embedding models (a set of five models ranging from 22 to 334 million …

Standard bag-of-words term-matching techniques in document retrieval fail to exploit rich
semantic information embedded in the document texts. One promising recent trend in …

Abstract The TREC Deep Learning tracks used MS MARCO Version 1 as their official
training data until 2020 and switched to Version 2 in 2021. For Version 2, all previously …

Contrastive learning has been the dominant approach to training dense retrieval models. In
this work, we investigate the impact of ranking context-an often overlooked aspect of …

In order to adopt deep learning for information retrieval, models are needed that can capture
all relevant information required to assess the relevance of a document to a given user …

While BERT has been shown to be effective for passage retrieval, its maximum input length
limitation poses a challenge when applying the model to document retrieval. In this work, we …

This is the first year of the TREC Product search track. The focus this year was the creation of
a reusable collection and evaluation of the impact of the use of metadata and multi-modal …