Papers › Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0

Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0

11 Apr 2022BigScience (ACL) 2022 5arXiv:2204.05211archive 2025-07-28

Francesco De Toni, Christopher Akiki, Javier de la Rosa, Clémentine Fourrier, Enrique Manjavacas, Stefan Schweter, Daniel van Strien

In this work, we explore whether the recently demonstrated zero-shot abilities of the T0 model extend to Named Entity Recognition for out-of-distribution languages and time periods. Using a historical newspaper corpus in 3 languages as test-bed, we use prompts to extract possible named entities. Our results show that a naive approach for prompt-based zero-shot multilingual Named Entity Recognition is error-prone, but highlights the potential of such an approach for historical languages lacking labeled datasets. Moreover, we also find that T0-like models can be probed to predict the publication date and language of a document, which could be very relevant for the study of historical texts.

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Multilingual Named Entity RecognitionNamed Entity RecognitionNamed Entity Recognition (NER)named-entity-recognition

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