Effectively identifying threats and mitigating their potential damage during crisis situations, such as natural disasters or violent attacks, is paramount for safeguarding endangered individuals. To tackle these challenges, AI has been used in assisting humans in emergency situations. Still, the use of NLP techniques remains limited and mostly focuses on classification tasks. The significant potential of timely warning message generation using NLG architectures, however, has been largely overlooked. In this paper we present CrisiText, the first large-scale dataset for the generation of warning messages across 13 different types of crisis scenarios. The dataset contains more than 400,000 warning messages (spanning almost 18,000 crisis situations) aimed at assisting civilians during and after such events. To generate the dataset, we started from existing crisis descriptions and created chains of events related to the scenarios. Each event was then paired with a warning message. The generations follow experts' written guidelines to ensure correct terminology and factuality of their suggestions. Additionally, each message is accompanied by three suboptimal warning types to allow for the study of different NLG approaches. To this end, we conducted a series of experiments comparing supervised fine-tuning setups with preference alignment, zero-shot, and few-shot approaches. We further assessed model performance in out-of-distribution scenarios and evaluated the effectiveness of an automatic post-editor.

翻译：在自然灾害或暴力袭击等危机情况下，有效识别威胁并减轻其潜在损害对于保护受威胁个体至关重要。为应对这些挑战，人工智能已被用于协助人类处理紧急情况。然而，自然语言处理技术的应用仍然有限，且主要集中在分类任务上。而利用自然语言生成架构及时生成预警信息的巨大潜力在很大程度上被忽视了。本文提出了CrisiText，这是首个面向13种不同类型危机场景生成预警消息的大规模数据集。该数据集包含超过40万条预警消息（涵盖近1.8万种危机情境），旨在为此类事件期间及之后的民众提供协助。为构建本数据集，我们从现有危机描述出发，创建了与场景相关的事件链。每个事件随后与一条预警消息配对。消息生成遵循专家书面指南，以确保术语准确性和建议的事实性。此外，每条消息均附带三种次优预警类型，以便研究不同的自然语言生成方法。为此，我们进行了一系列实验，比较监督微调设置与偏好对齐、零样本及少样本方法。我们进一步评估了模型在分布外场景下的性能，并检验了自动后编辑器的有效性。