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 …

Neural information retrieval (IR) has greatly advanced search and other knowledge-
intensive language tasks. While many neural IR methods encode queries and documents …

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 …

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 …

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 …

We integrate irdatasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval
Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and …

Long-context language models (LCLMs) have the potential to revolutionize our approach to
tasks traditionally reliant on external tools like retrieval systems or databases. Leveraging …

This paper is a condensed report on the second year of the Touché shared task on
argument retrieval held at CLEF 2021. With the goal to provide a collaborative platform for …

Query Reformulation (QR) is a set of techniques used to transform a user's original search
query to a text that better aligns with the user's intent and improves their search experience …

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 …