Are Neural Ranking Models Robust?
文献类型：期刊论文
| 作者 | Wu, Chen1,2; Zhang, Ruqing1,2; Guo, Jiafeng1,2; Fan, Yixing1,2; Cheng, Xueqi1,2 |
| 刊名 | ACM TRANSACTIONS ON INFORMATION SYSTEMS
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| 出版日期 | 2023-04-01 |
| 卷号 | 41期号:2页码:36 |
| 关键词 | Robustness Ranking Models Systematic Analysis |
| ISSN号 | 1046-8188 |
| DOI | 10.1145/3534928 |
| 英文摘要 | Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field. So far, much effort has been devoted to developing effective neural ranking models that can generalize well on new data. There has been less attention paid to the robustness perspective. Unlike the effectiveness, which is about the average performance of a system under normal purpose, robustness cares more about the system performance in the worst case or under malicious operations instead. When a new technique enters into the real-world application, it is critical to know not only how it works in average, but also how would it behave in abnormal situations. So, we raise the question in this work: Are neural ranking models robust? To answer this question, first, we need to clarify what we refer to when we talk about the robustness of ranking models in IR. We show that robustness is actually a multi-dimensional concept and there are three ways to define it in IR: (1) the performance variance under the independent and identically distributed (I.I.D.) setting; (2) the out-of-distribution (OOD) generalizability; and (3) the defensive ability against adversarial operations. The latter two definitions can be further specified into two different perspectives, respectively, leading to five robustness tasks in total. Based on this taxonomy, we build corresponding benchmark datasets, design empirical experiments, and systematically analyze the robustness of several representative neural ranking models against traditional probabilistic ranking models and learning-to-rank (LTR) models. The empirical results show that there is no simple answer to our question. While neural ranking models are less robust against other IR models in most cases, some of them can still win two out of five tasks. This is the first comprehensive study on the robustness of neural ranking models. We believe thewaywe study the robustness as well as our findings would be beneficial to the IR community. We will also release all the data and codes to facilitate the future research in this direction. |
| 资助项目 | National Natural Science Foundation of China (NSFC)[62006218] ; National Natural Science Foundation of China (NSFC)[61902381] ; National Natural Science Foundation of China (NSFC)[61872338] ; Youth Innovation Promotion Association CAS[20144310] ; Youth Innovation Promotion Association CAS[2021100] ; Foundation and Frontier Research Key Program of Chongqing Science and Technology Commission[cstc2017jcyjBX0059] ; Lenovo-CAS Joint Lab Youth Scientist Project |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000971779000004 |
| 出版者 | ASSOC COMPUTING MACHINERY |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21411]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Guo, Jiafeng; Cheng, Xueqi |
| 作者单位 | 1.Univ Chinese Acad Sci, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Chen,Zhang, Ruqing,Guo, Jiafeng,et al. Are Neural Ranking Models Robust?[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2023,41(2):36. |
| APA | Wu, Chen,Zhang, Ruqing,Guo, Jiafeng,Fan, Yixing,&Cheng, Xueqi.(2023).Are Neural Ranking Models Robust?.ACM TRANSACTIONS ON INFORMATION SYSTEMS,41(2),36. |
| MLA | Wu, Chen,et al."Are Neural Ranking Models Robust?".ACM TRANSACTIONS ON INFORMATION SYSTEMS 41.2(2023):36. |
入库方式： OAI收割
来源：计算技术研究所
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