%0 Conference Paper %T Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0 %A De Toni, Francesco; Akiki, Christopher; De La Rosa, Javier; Fourrier, Clémentine; Manjavacas, Enrique; Schweter, Stefan; Van Strien, Daniel %D 2022 %8 2022-12-13 %I Association for Computational Linguistics (ACL) %P 75-83 %U https://aclanthology.org/2022.bigscience-1.7 %R 10.18653/v1/2022.bigscience-1.7 %X 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. %G English %[ 2024-03-29 %9 Conference paper (published) %~ Hyku %W British Library