This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

Existing neural information retrieval (IR) models have often been studied in homogeneous
and narrow settings, which has considerably limited insights into their out-of-distribution …

Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

We present a new zero-shot dense retrieval (ZeroDR) method, COCO-DR, to improve the
generalization ability of dense retrieval by combating the distribution shifts between source …

In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever that does not
require any supervised data for training. Specifically, we first present Iterative Contrastive …

We present Event2018, a corpus annotated for event detection tasks, consisting of 38 million
tweets in French (retweets excluded) including more than 130,000 tweets manually …

Dense retrievers encode documents into fixed dimensional embeddings. However, storing
all the document embeddings within an index produces bulky indexes which are expensive …

Abstract Domain adaptation aims to transfer the knowledge learned on (data-rich) source
domains to (low-resource) target domains, and a popular method is invariant representation …

Retrieving relevant plots from the book for a query is a critical task, which can improve the
reading experience and efficiency of readers. Readers usually only give an abstract and …

In this paper we improve the zero-shot generalization ability of language models via Mixture-
Of-Memory Augmentation (MoMA), a mechanism that retrieves augmentation documents …