The probing classifiers framework has been employed for interpreting deep
neural network models for a variety of natural language processing (NLP)
applications. Studies, however, have largely focused on sentencelevel NLP
tasks. This work is the first to apply the probing paradigm to representations
learned for document-level information extraction (IE). We designed eight
embedding probes to analyze surface, semantic, and event-understanding
capabilities relevant to document-level event extraction. We apply them to the
representations acquired by learning models from three different LLM-based
document-level IE approaches on a standard dataset. We found that trained
encoders from these models yield embeddings that can modestly improve argument
detections and labeling but only slightly enhance event-level tasks, albeit
trade-offs in information helpful for coherence and event-type prediction. We
further found that encoder models struggle with document length and
cross-sentence discourse.

将探测分类器框架应用于文本级别信息提取，研究表明，通过深度神经网络模型得到的表示可以在一定程度上改进参数检测和标签化，并略微提升事件级别任务，尽管在连贯性和事件类型预测方面会有一些权衡，同时在处理文件长度和跨句子语篇方面仍存在一定的困难。