Name
Abstract | ||
|---|---|---|
The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to user's information need. Recently, the resurgence of deep learning has greatly advanced this field and leads to a hot topic named NeuIR (i.e., neural information retrieval), especially the paradigm of pre-training methods (PTMs). Owing to sophisticated pre-training objectives and huge model size, pre-trained models can learn universal language representations from massive textual data, which are beneficial to the ranking task of IR. Since there have been a large number of works dedicating to the application of PTMs in IR, we believe it is the right time to summarize the current status, learn from existing methods, and gain some insights for future development. In this survey, we present an overview of PTMs applied in different components of IR system, including the retrieval component, the re-ranking component, and other components. In addition, we also introduce PTMs specifically designed for IR, and summarize available datasets as well as benchmark leaderboards. Moreover, we discuss some open challenges and envision some promising directions, with the hope of inspiring more works on these topics for future research. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1561/1500000100 | Foundations and Trends in Information Retrieval |
DocType | Volume | Issue |
Journal | 16 | 3 |
ISSN | Citations | PageRank |
1554-0669 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yixing Fan | 1 | 202 | 19.39 |
| Xiaohui Xie | 2 | 40 | 7.49 |
| Yinqiong Cai | 3 | 0 | 1.35 |
| Jia Chen | 4 | 22 | 4.16 |
| Xinyu Ma | 5 | 8 | 1.53 |
| Xiangsheng Li | 6 | 25 | 3.84 |
| Ruqing Zhang | 7 | 15 | 10.40 |
| Jiafeng Guo | 8 | 1737 | 102.17 |
| Yiqun Liu | 9 | 1592 | 136.51 |