Rethinking the Event Coding Pipeline with Prompt Entailment
Clément Lefebvre, Niklas Stoehr · 2023
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 1 , 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 suitable answer candidates Z * = {"injured", "hurt"...} by treating the event description as premise and the filled templates as hypothesis in a textual entailment task.In a final step, the selected answer candidate can be mapped to its corresponding event type.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 codebook design tool 2 .We show that our approach is robust through several checks: perturbing the event description and prompt template, restricting the vocabulary and removing contextual information.