Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework
This work addresses the problem of reducing annotation costs for entity recognition in document images, which is important for applications like document analysis, but it is incremental as it builds on existing sequence-to-sequence methods with novel label integration.
The paper tackles few-shot entity recognition in document images by proposing a label-aware sequence-to-sequence framework (LASER) that leverages label surface names and spatial embeddings to enhance semantic and layout correspondence, achieving superior performance on two benchmark datasets under few-shot conditions.
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.