Towards Query Performance Prediction for Neural Information Retrieval: Challenges and Opportunities

PROCEEDINGS OF THE 2023 ACM SIGIR INTERNATIONAL CONFERENCE ON THE THEORY OF INFORMATION RETRIEVAL, ICTIR 2023(2023)

引用 0|浏览22
暂无评分
摘要
In this work, we propose a novel framework to devise features that can be used by Query Performance Prediction (QPP) models for Neural Information Retrieval (NIR). Using the proposed framework as a periodic table of QPP components, practitioners can devise new predictors better suited for NIR. Through the framework, we detail what challenges and opportunities arise for QPPs at different stages of the NIR pipeline. We show the potential of the proposed framework by using it to devise two types of novel predictors. The first one, named MEMory-based QPP (MEM-QPP), exploits the similarity between test and train queries to measure how much a NIR system can memorize. The second adapts traditional QPPs into NIR-oriented ones by computing the query-corpus semantic similarity. By exploiting the inherent nature of NIR systems, the proposed predictors overcome, under various setups, the current State of the Art, highlighting - at the same time - the versatility of the framework in describing different types of QPPs.
更多
查看译文
关键词
Query Performance Prediction,Neural IR,QPP Framework
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要