Question Answering (QA) is a longstanding challenge in natural language
processing. Existing QA works mostly focus on specific question types,
knowledge domains, or reasoning skills. The specialty in QA research hinders
systems from modeling commonalities between tasks and generalization for wider
applications. To address this issue, we present ProQA, a unified QA paradigm
that solves various tasks through a single model. ProQA takes a unified
structural prompt as the bridge and improves the QA-centric ability by
structural prompt-based pre-training. Through a structurally designed
prompt-based input schema, ProQA concurrently models the knowledge
generalization for all QA tasks while keeping the knowledge customization for
every specific QA task. Furthermore, ProQA is pre-trained with structural
prompt-formatted large-scale synthesized corpus, which empowers the model with
the commonly-required QA ability. Experimental results on 11 QA benchmarks
demonstrate that ProQA consistently boosts performance on both full data
fine-tuning, few-shot learning, and zero-shot testing scenarios. Furthermore,
ProQA exhibits strong ability in both continual learning and transfer learning
by taking the advantages of the structural prompt.

ProQA 是一个统一的 QA 范式，通过结构提示进行预训练，可以在所有 QA 任务中同时建模知识概括，同时保持每个特定 QA 任务的知识定制。实验证明其在 11 个 QA 基准上展示了强大的性能提升，并在连续学习和迁移学习等方面表现出强大的能力。