论文标题
通过增强框来改善文档图像理解
Improving Document Image Understanding with Reinforcement Finetuning
论文作者
论文摘要
成功的人工智能系统通常需要大量标记的数据来从文档图像中提取信息。在本文中，我们研究了改善人工智能系统在理解文档图像中的性能的问题，尤其是在培训数据受到限制的情况下。我们通过使用增强学习提出一种新颖的填充方法来解决问题。我们的方法将信息提取模型视为策略网络，并使用策略梯度培训来更新模型，以最大程度地提高奖励功能，以补充传统的跨凝结损失。我们使用标签和专家反馈在四个数据集上进行的实验表明，我们的固定机制始终提高最先进的信息提取器的性能，尤其是在小型培训数据制度中。
Successful Artificial Intelligence systems often require numerous labeled data to extract information from document images. In this paper, we investigate the problem of improving the performance of Artificial Intelligence systems in understanding document images, especially in cases where training data is limited. We address the problem by proposing a novel finetuning method using reinforcement learning. Our approach treats the Information Extraction model as a policy network and uses policy gradient training to update the model to maximize combined reward functions that complement the traditional cross-entropy losses. Our experiments on four datasets using labels and expert feedback demonstrate that our finetuning mechanism consistently improves the performance of a state-of-the-art information extractor, especially in the small training data regime.
