A Deep Look into neural ranking models for information retrieval. Issue 6 (November 2020)

• Record Type: Journal Article
• Authors: Guo, Jiafeng; Fan, Yixing; Pang, Liang; Yang, Liu; Ai, Qingyao; Zamani, Hamed; Wu, Chen; Croft, W. Bruce; Cheng, Xueqi
• Is Part Of: Information processing & management. Volume 57:Issue 6(2020:Nov.)
• Publication Date: 2020-11
• Subjects: Neural ranking model -- Information retrieval -- Survey; 00-01 -- 99-00; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2019.102067
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We introduce some typical IR tasks addressed by neural ranking models and discuss major characteristics and challenges. We introduce a unified formulation over neural ranking models, and review existing models based on this formulation. We survey published empirical results on the ad-hoc retrieval and QA tasks to conduct a comprehensive comparison. We discuss several trending topics that are important or might be promising in the future. Abstract: Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions.