Multilingual Event Extraction from Historical Newspaper Adverts
Nadav Borenstein Natalia da Silva Perez Isabelle Augenstein
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.