  Visually-rich document entity retrieval (VDER), which extracts key
information (e.g. date, address) from document images like invoices and
receipts, has become an important topic in industrial NLP applications. The
emergence of new document types at a constant pace, each with its unique entity
types, presents a unique challenge: many documents contain unseen entity types
that occur only a couple of times. Addressing this challenge requires models to
have the ability of learning entities in a few-shot manner. However, prior
works for Few-shot VDER mainly address the problem at the document level with a
predefined global entity space, which doesn't account for the entity-level
few-shot scenario: target entity types are locally personalized by each task
and entity occurrences vary significantly among documents. To address this
unexplored scenario, this paper studies a novel entity-level few-shot VDER
task. The challenges lie in the uniqueness of the label space for each task and
the increased complexity of out-of-distribution (OOD) contents. To tackle this
novel task, we present a task-aware meta-learning based framework, with a
central focus on achieving effective task personalization that distinguishes
between in-task and out-of-task distribution. Specifically, we adopt a
hierarchical decoder (HC) and employ contrastive learning (ContrastProtoNet) to
achieve this goal. Furthermore, we introduce a new dataset, FewVEX, to boost
future research in the field of entity-level few-shot VDER. Experimental
results demonstrate our approaches significantly improve the robustness of
popular meta-learning baselines.
