Go to ACL ARR 2026 January homepageA Dual-View Analysis of Multiple Languages in Colonial Newspapers
Keywords: multilingual tasks, historical analysis, semantic shift.
Abstract: Historical newspapers from the colonial period offer valuable evidence of how racializing language evolved over time. However, there are challenges in studying this type of historical data: 1) Data scarcity: acquiring large, annotated historical datasets is difficult, hindering the possibility of analyzing racialization comprehensively; 2) Digitized materials frequently contain Optical Character Recognition (OCR) errors and other types of noise that complicate text extraction and computational analysis; 3) Colonial newspapers are often multilingual and written in archaic prose, hindering the effectiveness of NLP tools developed for modern, single language texts. This paper addresses these challenges by conducting a dual-view, jointly studying multilingual event extraction and temporal semantic shift tasks. Specifically, we introduce a contextual question answering (CQA) and a visual question answering (VQA) derived from eighteenth- and nineteenth-century colonial newspapers, which aims to capture the idiosyncratic characteristics of this material. Content-wise, we focus on how enslaved people were described by enslavers as well as how they articulated their own condition through QA pairs of newspapers written in Dutch, English-French, and Spanish. Our results show that LLMs are still limited for low-resource VQA tasks. For temporal semantic change, we train temporal word embedding with a compass. The study concludes that racialization is a fluid process of linguistic recalibration where the decline of slavery merely shifted the language of control onto new categories of labor and identity, leaving a "semantic debt" that still shapes language bias today.
Paper Type: Long
Research Area: Multilinguality and Language Diversity
Research Area Keywords: multilingual extraction,colonial languages,less-resourced languages,language/cultural bias analysis
Contribution Types: Data analysis
Languages Studied: English, Dutch, French, Spanish
Submission Number: 4080
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