论文标题
MICO：与共同培训的选择性搜索
MICO: Selective Search with Mutual Information Co-training
论文作者
论文摘要
与传统的详尽搜索相反，选择性搜索的第一个群集文档将文档分为几个组，然后通过查询对所有文档进行详尽的搜索，以限制一个组或仅几组中执行的搜索。选择性搜索旨在减少现代大型搜索系统中的延迟和计算。在这项研究中，我们提出了MICO，MICO是一个相互信息共同培训框架，用于使用搜索日志的最小监督进行选择性搜索。经过培训，MICO不仅会将文档聚集，还可以将看不见的查询路由到相关群集以进行有效检索。在我们的经验实验中，MICO显着提高了选择性搜索的多个指标的性能，并且表现优于许多现有的竞争基线。
In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups. Selective search is designed to reduce the latency and computation in modern large-scale search systems. In this study, we propose MICO, a Mutual Information CO-training framework for selective search with minimal supervision using the search logs. After training, MICO does not only cluster the documents, but also routes unseen queries to the relevant clusters for efficient retrieval. In our empirical experiments, MICO significantly improves the performance on multiple metrics of selective search and outperforms a number of existing competitive baselines.
