Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

Recently, the emergence of pre-trained models (PTMs) has brought natural language
processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs …

Industrial recommender systems have been growing increasingly complex, may
involve\emph {diverse domains} such as e-commerce products and user-generated …

A vital step towards the widespread adoption of neural retrieval models is their resource
efficiency throughout the training, indexing and query workflows. The neural IR community …

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 …

Current state-of-the-art document retrieval solutions mainly follow an index-retrieve
paradigm, where the index is hard to be directly optimized for the final retrieval target. In this …

Despite pre-training's progress in many important NLP tasks, it remains to explore effective
pre-training strategies for dense retrieval. In this paper, we propose RetroMAE, a new …

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 …

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 …

Retrieval and ranking models are the backbone of many applications such as web search,
open domain QA, or text-based recommender systems. The latency of neural ranking …