NLP methods can aid historians in analyzing textual materials in greater volumes than manually feasible. Developing such methods poses substantial challenges though. First, acquiring large, annotated historical datasets is difficult, as only domain experts can reliably label them. Second, most available off-the-shelf NLP models are trained on modern language texts, rendering them significantly less effective when applied to historical corpora. This is particularly problematic for less well studied tasks, and for languages other than English. This paper addresses these challenges while focusing on the under-explored task of event extraction from a novel domain of historical texts. We introduce a new multilingual dataset in English, French, and Dutch composed of newspaper ads from the early modern colonial period reporting on enslaved people who liberated themselves from enslavement. We find that: 1) even with scarce annotated data, it is possible to achieve surprisingly good results by formulating the problem as an extractive QA task and leveraging existing datasets and models for modern languages; and 2) cross-lingual low-resource learning for historical languages is highly challenging, and machine translation of the historical datasets to the considered target languages is, in practice, often the best-performing solution.

翻译：NLP方法能够帮助历史学家以远超人工处理的规模分析文本材料。然而，开发此类方法面临重大挑战。首先，获取大规模标注的历史数据集十分困难，因为只有领域专家能够可靠地标注这些数据。其次，大多数现成的NLP模型基于现代语言文本训练，在应用于历史语料库时效果显著下降。这一问题对于研究较少的任务及英语以外的语言尤为突出。本文聚焦于尚未充分探索的任务——从历史文本的新领域中提取事件，以应对上述挑战。我们引入了一个新的多语言数据集，涵盖英语、法语和荷兰语，由近代早期殖民时期的报纸广告组成，这些广告报道了从奴役中自我解放的奴隶。我们发现：1）即使标注数据稀缺，通过将该问题构建为抽取式问答任务，并利用现有数据集和针对现代语言的模型，仍能取得令人惊讶的良好效果；2）历史语言的跨语言低资源学习极具挑战性，而实践中，将历史数据集机器翻译至目标语言往往是表现最佳的解决方案。