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。

对比训练提高半结构化文档零样本分类能力

Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents

Social media such as tweets are emerging as platforms contributing to
situational awareness during disasters. Information shared on Twitter by both
affected population (e.g., requesting assistance, warning) and those outside
the impact zone (e.g., providing assistance) would help first responders,
decision makers, and the public to understand the situation first-hand.
Effective use of such information requires timely selection and analysis of
tweets that are relevant to a particular disaster. Even though abundant tweets
are promising as a data source, it is challenging to automatically identify
relevant messages since tweet are short and unstructured, resulting to
unsatisfactory classification performance of conventional learning-based
approaches. Thus, we propose a simple yet effective algorithm to identify
relevant messages based on matching keywords and hashtags, and provide a
comparison between matching-based and learning-based approaches. To evaluate
the two approaches, we put them into a framework specifically proposed for
analyzing disaster-related tweets. Analysis results on eleven datasets with
various disaster types show that our technique provides relevant tweets of
higher quality and more interpretable results of sentiment analysis tasks when
compared to learning approach.

本文研究了在灾难中使用社交媒体如 Twitter 等传播信息以提高灾难控制。当前流行的通过学习的方法无法很好地识别与灾难相关的推文，因此我们提出了一种基于匹配关键词和标签的简单而有效的算法。结果表明，我们的方法提供了更高质量和更可解释的情绪分析任务相关的推文，是一种可行的方法。