Multilingual Event Extraction from Historical Newspaper Adverts (2023)

Abstract: 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.

## introduction analyzing large corpora of historical documents can provide invaluable insights on past events in multiple resolutions, from the life of an individual to processes on a global scale @xcite @xcite . while historians traditionally work closely with the texts they study, automating parts of the analysis using nlp tools can help speed up the research process and facilitate the extraction of historical evidence from large corpora, allowing historians to focus on interpretation. however, building nlp models for historical texts poses a substantial challenge. first, acquiring large, annotated historical datasets is difficult @xcite , as only domain experts can reliably label them. this renders the default fully-supervised learning setting less feasible for historical corpora. compounding this, most off-the-shelf nlp models were trained on modern language texts and display significantly weaker performance for historical documents @xcite @xcite , which usually suffer from a high rate of ocr errors and are written in a substantially different language. this is particularly challenging for less well-studied tasks or for non-english languages. one of these under-explored tasks is event extraction from historical texts @xcite , which can aid in retrieving information about complex events from vast amounts of texts. here, we research extraction of events from adverts in colonial newspapers reporting on enslaved people who escaped their enslavers. studying these ads can shed light on the linguistic processes of racialization during the early modern colonial period (c. 1450 to 1850), the era of the transatlantic slave trade, which coincided with the early era of mass print media. methodologically, we research low-resource learning methods for event extraction, for which only a handful of prior papers exist @xcite . to the best of our knowledge, this is the first paper to study historical event extraction in a multilingual setting. specifically, our contributions are as follows: run away from a ship in the port of weymouth, belonging to dorchester, mr william ward commander, bound for newfoundland, philip mardery a negro, aged 22 years middle sized man, with a close bodied frize coat lined, buttons of the same, a small cape, a great coat not lined, with blew shirts fitting for the sea; all gentlemen captains, or masters of ships, that shall happen to have any such offered to them, as a servant, are desired to give notice to mr. walters grocer in westminster; or to mr. killman apothecary in sarum; or to mr. turner, at his coffee house in dorchester, and they shall be well rewarded with charges a) b) newspaper title post boy (1695) newspaper date 11 april 1700 given name philip given surname mardery racial descriptor negro clothing a close bodied frize coat lined, buttons of the same, a small cape, a great coat not lined, with blew shirts fitting for the sea contact address westminster; sarum; at his coffee house in dorchester total reward well rewarded with charges away slave adverts" (newman et al., 2019). foot_0 fig. 1a contains an example ad. ## methods we now describe the methodology of the paper, including problem formulation ( §3.1), datasets ( §3.2), models ( §3.3), and the experiments setup ( §3.4). ## conclusions in this work, we address the unique challenges of event extraction from historical texts in different languages. we start by developing a new multilingual dataset in english, french, and dutch of events, consisting of newspaper adverts reporting on enslaved people escaping their enslavers. we then demonstrate the benefits of framing the problem as an extractive qa task. we show that even with scarcely annotated data, this formulation can achieve surprisingly good results by leveraging existing datasets and models for modern languages. finally, we show that 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.