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 …

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 …

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 …

The MS MARCO ranking dataset has been widely used for training deep learning models for
IR tasks, achieving considerable effectiveness on diverse zero-shot scenarios. However, this …

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 …

While much recent work has demonstrated that hard negative mining can be used to train
better bi-encoder models, few have considered it in the context of cross-encoders, which are …

Dense retrieval models using a transformer-based bi-encoder architecture have emerged as
an active area of research. In this article, we focus on the task of monolingual retrieval in a …

The COVID-19 pandemic has brought about a proliferation of harmful news articles online,
with sources lacking credibility and misrepresenting scientific facts. Misinformation has real …

Neural retrieval models are generally regarded as fundamentally different from the retrieval
techniques used in the late 1990's when the TREC ad hoc test collections were constructed …

Dense passage retrieval (DPR) models show great effectiveness gains in first stage retrieval
for the web domain. However in the web domain we are in a setting with large amounts of …