A Deep Cascade Model for Multi-Document Reading Comprehension

Understand

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.