We investigate semi-structured document classification in a zero-shot
setting. Classification of semi-structured documents is more challenging than
that of standard unstructured documents, as positional, layout, and style
information play a vital role in interpreting such documents. The standard
classification setting where categories are fixed during both training and
testing falls short in dynamic environments where new document categories could
potentially emerge. We focus exclusively on the zero-shot setting where
inference is done on new unseen classes. To address this task, we propose a
matching-based approach that relies on a pairwise contrastive objective for
both pretraining and fine-tuning. Our results show a significant boost in Macro
F$_1$ from the proposed pretraining step in both supervised and unsupervised
zero-shot settings.

本研究使用了匹配方法，并对零样本学习中的半结构化文档分类任务进行了探索，使用对位对比目标来加强监督和无监督的零样本学习，显著提高了宏 F1 的结果。