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# How BERT Speaks Shakespearean English? Evaluating Historical Bias in Contextual Language Models

**Authors:** Miriam Cuscito, Alfio Ferrara, Martin Ruskov · **Year:** 2024 · **Venue:** arXiv:2402.05034 **Link:** [arxiv.org/abs/2402.05034](https://arxiv.org/abs/2402.05034) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**2** citations](https://openalex.org/works?filter=cites:W4391670418) openalex · 2026-10-18[**2** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/cuscito-2024-bert-speaks-shakespearean-english-evaluating/) of the 140673 papers whose bibliographies we read[arXiv:2402.05034](https://openalex.org/S4306400194)[licence](http://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://arxiv.org/abs/2402.05034)
- [PDF](https://arxiv.org/pdf/2402.05034)
- [arXiv 2402.05034](https://arxiv.org/abs/2402.05034)
- [OpenAlex](https://openalex.org/W4391670418)

**Authors (3)**

1. [Miriam Cuscito](https://openalex.org/A5093895430) [ORCID](https://orcid.org/0009-0003-9585-2803) · [Semantic Scholar](https://www.semanticscholar.org/author/2283137809) · h 1 · 2 citations · 6 works
2. [Alfio Ferrara](https://openalex.org/A5062638440) [ORCID](https://orcid.org/0000-0002-4991-4984) · [Semantic Scholar](https://www.semanticscholar.org/author/2283139135) · h 26 · 2619 citations · 201 works
3. [Martin Ruskov](https://openalex.org/A5077205990) [ORCID](https://orcid.org/0000-0001-5337-0636) · [Semantic Scholar](https://www.semanticscholar.org/author/2285169574) · h 8 · 208 citations · 121 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper asks whether contextual language models know the difference between Early Modern English and Modern English. It builds a test of 60 sentences with one masked word each: 20 marked for Early Modern English, 20 for Modern English and 20 neutral. Each sentence gets a temporal score on a five-point scale, set by hand from the linguistic markedness of the sentence. Three BERT-family models fill each mask, and each candidate word receives a score for its era in its own sentence context, not in isolation. The results are weighted into a bias score and an adequacy score. MacBERTh, trained on 1500 to 1950 texts, is most aligned with the early sentences. Plain BERT is most aligned with the modern ones. On neutral sentences all models do well. The authors call the study preliminary: one test set, three models, hand-set labels.

## Ideas

- I1. Era audit of the standard. Score the standard encoder's chunk candidates for each concept with an era value taken from the author corpora, using a count ratio: how often the word appears in early-author text against modern text. On the Bible pairs this shows which standard choices are archaic, such as *hath* or *unto* (open problem 6).
- I2. Score in context, not globally. The paper shows that *you* is neutral in general but archaic in a particular sentence. The encoder already picks the most specific context seen often enough, so the era audit should use the same context key.
- I3. Use the adequacy score to check an author delta. After a delta applies, output on held-out verses should move toward the target era. A drop would show the delta is not doing what its labels say.
- I4. Treat the masked model as a teacher only, labelling candidate sets offline. The SLM answers at test time from counts.

## For Mova

The code is `tools/translate` (`profile.rs`, `measure.rs`). The neural masked-language model is not for Mova, but the era-scoring idea carries over in count form. It gives the translator a way to find archaic choices in the standard without hand lists, and an adequacy check for deltas on held-out verses.

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 2402.05034](https://arxiv.org/abs/2402.05034)

```
@misc{cuscito2024how,
  title = {{How BERT Speaks Shakespearean English? Evaluating Historical Bias in Contextual Language Models}},
  author = {Cuscito, Miriam and Ferrara, Alfio and Ruskov, Martin},
  year = {2024},
  eprint = {2402.05034},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2402.05034}
}
```

- [← previous Translation using Minimal Recursion Semantics](https://md.mova.elamur.ai/free/en/docs/papers/copestake-1995-translation-minimal-recursion-semantics/)
- [next → Crossroads between contrastive linguistics, translation studies and machine translation: TC3-II](https://md.mova.elamur.ai/free/en/docs/papers/czulo-2017-crossroads-between-contrastive-linguistics/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

## References

14 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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7. Alba Morollon Diaz-Faes, Carla Murteira, and Martin Ruskov. 2023. Explicit references to social values in fairy tales: A comparison between three european cultures. In The Joint 3rd International Conference on Natural Language Processing for Digital Humanities & 8th International Workshop on Computational Linguistics for Uralic Languages, Tokyo, Japan. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Alba+Morollon+Diaz%2DFaes%2C+Carla+Murteira%2C+and+Martin+Ruskov%2E+2023%2E+Explicit+references+to+social+values+in+fairy+tales%3A+A+comparison+between+three+european+cultures%2E+In+The+Joint+3rd+International+Conf)
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