论文标题
迈向文档图像中的几个射击实体识别：标签感知序列到序列框架
Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework
论文作者
论文摘要
实体识别是理解文档图像的基本任务。传统的序列标签框架将实体类型视为类ID，并依靠广泛的数据和高质量的注释来学习通常在实践中昂贵的语义。在本文中，我们旨在建立一个仅需要几张带注释的文档图像的实体识别模型。为了克服数据限制，我们建议利用标签表面名称来更好地告知目标实体类型语义的模型，并将标签嵌入空间嵌入空间中，以捕获区域和标签之间的空间对应关系。具体而言，我们超越了序列标签，并开发了一种新颖的标签seq2Seq框架激光。提出的模型遵循了一个新的标签方案，该方案在生成实体后明确生成标签表面名称。在训练过程中，激光通过更新标签表面名称表示并增强标签区域相关性来完善标签语义。通过这种方式，激光通过语义和布局信件识别来自文档图像的实体。在两个基准数据集上进行的广泛实验证明了激光在几个射击设置下的优越性。
Entity recognition is a fundamental task in understanding document images. Traditional sequence labeling frameworks treat the entity types as class IDs and rely on extensive data and high-quality annotations to learn semantics which are typically expensive in practice. In this paper, we aim to build an entity recognition model requiring only a few shots of annotated document images. To overcome the data limitation, we propose to leverage the label surface names to better inform the model of the target entity type semantics and also embed the labels into the spatial embedding space to capture the spatial correspondence between regions and labels. Specifically, we go beyond sequence labeling and develop a novel label-aware seq2seq framework, LASER. The proposed model follows a new labeling scheme that generates the label surface names word-by-word explicitly after generating the entities. During training, LASER refines the label semantics by updating the label surface name representations and also strengthens the label-region correlation. In this way, LASER recognizes the entities from document images through both semantic and layout correspondence. Extensive experiments on two benchmark datasets demonstrate the superiority of LASER under the few-shot setting.
