Recent studies on open-domain question answering have achieved prominent
performance improvement using pre-trained language models such as BERT.
State-of-the-art approaches typically follow the "retrieve and read" pipeline
and employ BERT-based reranker to filter retrieved documents before feeding
them into the reader module. The BERT retriever takes as input the
concatenation of question and each retrieved document. Despite the success of
these approaches in terms of QA accuracy, due to the concatenation, they can
barely handle high-throughput of incoming questions each with a large
collection of retrieved documents. To address the efficiency problem, we
propose DC-BERT, a decoupled contextual encoding framework that has dual BERT
models: an online BERT which encodes the question only once, and an offline
BERT which pre-encodes all the documents and caches their encodings. On SQuAD
Open and Natural Questions Open datasets, DC-BERT achieves 10x speedup on
document retrieval, while retaining most (about 98%) of the QA performance
compared to state-of-the-art approaches for open-domain question answering.

本文提出一种名为 DC-BERT 的上下文解码框架，实现了问题回答过程中高效的文档检索，并在 SQuAD Open 和 Natural Questions Open 数据集上取得了比现有技术方法更快和准确度稍低（QA 表现保持 98%）的结果。