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In this paper, we propose Docprompt for document question answering taskswith powerful zero-shot and few-shot performance. We proposed a novel weaklysupervised data generation method, a novel multl-stage training method and anovel understanding model \& generation model ensemble method. We achievedstate-of-the-art performance on 4 document question answering tasks. Thismethod greatly improves the delivery efficiency and model performance ofdocument question answering customer projects, reducing annotation costs andlabor costs. Our demo can be found athttps://huggingface.co/spaces/PaddlePaddle/ERNIE-Layout.

DocPrompt: Large-scale continue pretrain for zero-shot and few-shot document question answering