For monitoring crises, political events are extracted from the news. The
large amount of unstructured full-text event descriptions makes a case-by-case
analysis unmanageable, particularly for low-resource humanitarian aid
organizations. This creates a demand to classify events into event types, a
task referred to as event coding. Typically, domain experts craft an event type
ontology, annotators label a large dataset and technical experts develop a
supervised coding system. In this work, we propose PR-ENT, a new event coding
approach that is more flexible and resource-efficient, while maintaining
competitive accuracy: first, we extend an event description such as "Military
injured two civilians'' by a template, e.g. "People were [Z]" and prompt a
pre-trained (cloze) language model to fill the slot Z. Second, we select answer
candidates Z* = {"injured'', "hurt"...} by treating the event description as
premise and the filled templates as hypothesis in a textual entailment task.
This allows domain experts to draft the codebook directly as labeled prompts
and interpretable answer candidates. This human-in-the-loop process is guided
by our interactive codebook design tool. We evaluate PR-ENT in several
robustness checks: perturbing the event description and prompt template,
restricting the vocabulary and removing contextual information.

本文提出了一种名为 PR-ENT 的事件编码新方法，通过引入预训练语言模型对事件描述进行扩展，并将事件描述视为前提，填充的模板作为假设，从中提取可解释的答案，从而可直接起草编码手册，并通过交互式编码手册设计工具进行人机交互，从而更灵活、高效地对事件进行编码。