A fundamental trade-off between effectiveness and efficiency needs to be
balanced when designing an online question answering system. Effectiveness
comes from sophisticated functions such as extractive machine reading
comprehension (MRC), while efficiency is obtained from improvements in
preliminary retrieval components such as candidate document selection and
paragraph ranking. Given the complexity of the real-world multi-document MRC
scenario, it is difficult to jointly optimize both in an end-to-end system. To
address this problem, we develop a novel deep cascade learning model, which
progressively evolves from the document-level and paragraph-level ranking of
candidate texts to more precise answer extraction with machine reading
comprehension. Specifically, irrelevant documents and paragraphs are first
filtered out with simple functions for efficiency consideration. Then we
jointly train three modules on the remaining texts for better tracking the
answer: the document extraction, the paragraph extraction and the answer
extraction. Experiment results show that the proposed method outperforms the
previous state-of-the-art methods on two large-scale multi-document benchmark
datasets, i.e., TriviaQA and DuReader. In addition, our online system can
stably serve typical scenarios with millions of daily requests in less than
50ms.

本研究提出一种深度串级学习模型来平衡在线问答系统的效能和效率之间的基本权衡，在多文档的 MRC 场景中取得更好的效果并能够在不到 50 毫秒内稳定地为数百万日请求提供服务。