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Neural information retrieval: at the end of the early years

Onal, Kezban Dilek
Zhang, Ye
Altıngövde, İsmail Sengör
Rahman, Md Mustafizur
Karagöz, Pınar
Braylan, Alex
Dang, Brandon
Chang, Heng-Lu
Kim, Henna
McNamara, Quinten
Angert, Aaron
Banners, Edward
Khetan, Vivek
McDonnell, Tyler
An Thanh Nguyen, An Thanh Nguyen
Xu, Dan
Wallace, Byron C.
de Rijke, Maarten
Lease, Matthew
A recent "third wave'' of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper, we survey the current landscape of Neural IR research, paying special attention to the use of learned distributed representations of textual units. We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.