Neural ranking models for document retrieval

TL;DR

This paper reviews and compares various neural ranking models for document retrieval, highlighting their contributions, limitations, and future research directions in leveraging deep learning for information retrieval tasks.

Contribution

It provides a comprehensive comparison of neural ranking models, analyzing their components, and discusses future research directions in deep learning-based information retrieval.

Findings

Neural models outperform traditional methods in some retrieval tasks.

Different neural components contribute uniquely to ranking performance.

The paper draws analogies between document retrieval and other structured retrieval tasks.

Abstract

Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.