How BERT Speaks Shakespearean English? Evaluating Historical Bias in Contextual Language Models
Not on the map. Why?
Abstract (arXiv)
In this paper, we explore the idea of analysing the historical bias of contextual language models based on BERT by measuring their adequacy with respect to Early Modern (EME) and Modern (ME) English. In our preliminary experiments, we perform fill-in-the-blank tests with 60 masked sentences (20 EME-specific, 20 ME-specific and 20 generic) and three different models (i.e., BERT Base, MacBERTh, English HLM). We then rate the model predictions according to a 5-point bipolar scale between the two language varieties and derive a weighted score to measure the adequacy of each model to EME and ME varieties of English.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 7 Feb 2024.
Builds on
5 of 13 references.
References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT modelcited by 87
- 2021Mitigating Language-Dependent Ethnic Bias in BERTcited by 87
- 2021Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Modelscited by 57
- 2023A Survey on Contextualised Semantic Shift Detectioncited by 21
Built on by
Citing papers per year: 2024: 2.
2 papers.