Although considerable attention has been given to neural ranking
architectures recently, far less attention has been paid to the term
representations that are used as input to these models. In this work, we
investigate how two pretrained contextualized language models (ELMo and BERT)
can be utilized for ad-hoc document ranking. Through experiments on TREC
benchmarks, we find that several existing neural ranking architectures can
benefit from the additional context provided by contextualized language models.
Furthermore, we propose a joint approach that incorporates BERT's
classification vector into existing neural models and show that it outperforms
state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR
(Contextualized Embeddings for Document Ranking). We also address practical
challenges in using these models for ranking, including the maximum input
length imposed by BERT and runtime performance impacts of contextualized
language models.

该研究旨在探究如何利用预训练的上下文化语言模型（ELMo 和 BERT）进行即席文档排名，并提出了一种联合方法，称为 CEDR（用于文档排名的上下文化嵌入），证明它优于现有的排名基线。