Multilingual Event Extraction from Historical Newspaper Adverts

Nadav Borenstein*, Natália da Silva Perez, Isabelle Augenstein

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

2 Citations (Scopus)
6 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationProceedings of the 61st Annual Meeting of the Association for Computational Linguistics
Place of PublicationToronto, Canada
Pages10304–10325
Number of pages22
Volume1
EditionLong Papers
ISBN (Electronic)9781959429722
DOIs
Publication statusPublished - 14 Jul 2023

Bibliographical note

©2023 Association for Computational Linguistics

Research programs

  • ESHCC HIS

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