Erasing Slavery: Memory, History, and Race in New England

Kellow, Margaret M. R.

doi:10.1353/rah.1999.0080

Cited by 1 publication

(1 citation statement)

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“…We deem it appropriate to briefly discuss the ethical considerations that are implied by such a usage. First, if a model can be used in a context for which it was not explicitly intended for, it stands to reason that it can be misused in that same context: while recognizing entities in historical texts might at first glance seem innocuous, numerous studies focused on BIPOC representation in history have shown that this is not the case, as some marginalized groups tend to suffer from history erasure (Kellow, 1999;Ram, 2020;Stanley, 2021). Second, the automation and scaling of historical inquiry could potentially lead to unreflected (mis)interpretations of the past (Gibbs and Owens, 2013;Gibbs, 2016).…”

Section: Broader Impacts Statementmentioning

confidence: 99%

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

Toni¹,

Akiki²,

Rosa³

et al. 2022

Proceedings of BigScience Episode #5 -- Workshop on Challenges &Amp; Perspectives in Creating Large Language Models

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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 errorprone, 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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Section: Broader Impacts Statementmentioning

confidence: 99%