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Question Answering (QA) is a longstanding challenge in natural languageprocessing. Existing QA works mostly focus on specific question types,knowledge domains, or reasoning skills. The specialty in QA research hinderssystems from modeling commonalities between tasks and generalization for widerapplications. To address this issue, we present ProQA, a unified QA paradigmthat solves various tasks through a single model. ProQA takes a unifiedstructural prompt as the bridge and improves the QA-centric ability bystructural prompt-based pre-training. Through a structurally designedprompt-based input schema, ProQA concurrently models the knowledgegeneralization for all QA tasks while keeping the knowledge customization forevery specific QA task. Furthermore, ProQA is pre-trained with structuralprompt-formatted large-scale synthesized corpus, which empowers the model withthe commonly-required QA ability. Experimental results on 11 QA benchmarksdemonstrate that ProQA consistently boosts performance on both full datafine-tuning, few-shot learning, and zero-shot testing scenarios. Furthermore,ProQA exhibits strong ability in both continual learning and transfer learningby taking the advantages of the structural prompt.

ProQA: Structural Prompt-based Pre-training for Unified Question Answering