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Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models
Open accessAbstract
Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose.However, applying stylistic variations is still largely a manual process, and there have been little efforts towards automating it.In this paper we explore automated methods to transform text from modern English to Shakespearean English using an end to end trainable neural model with pointers to enable copy action.To tackle limited amount of parallel data, we pre-train embeddings of words by leveraging external dictionaries mapping Shakespearean words to modern English words as well as additional text.Our methods are able to get a BLEU score of 31+, an improvement of ≈ 6 points over the strongest baseline.We publicly release our code to foster further research in this area.1
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Traditional methods for imitating language styles train models on a large amount of data, which leads to high costs.
“Traditional methods mainly focus on using a large volume of the person's text to train models.”
From Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style (2024) · cites this paper as [13]
The Abstract discusses a limited amount of parallel data, and §4.1 describes using additional text to pretrain embeddings. Neither passage says that traditional style-imitation methods mainly train on large volumes of a person's writing or that doing so incurs high costs; searches for those points found no supporting passage. (LLM, conf=0.72)
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MethodInfluentialGet To The Point: Summarization with Pointer-Generator Networks
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Cited by
Sorted by citations
Challenges and Strategies in Cross-Cultural NLP
Hershcovich, D., Frank, S., Lent, H., de Lhoneux, M., Abdou, M., Brandl, S., Bugliarello, E., Cabello Piqueras, L., Chalkidis, I., Cui, R., et al. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.482 · Cited by 99
Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style
Chen, Z., & Moscholios, S. (2024) · arXiv · doi:10.48550/arxiv.2410.03848
“[13] used an end-to-end neural network combined with network pointer techniques to transform texts into a Shakespearean style, successfully achieving the style imitation.”
Method
Metadata
- Authors
- Jhamtani, H., Gangal, V., Hovy, E., & Nyberg, E.
- Affiliations
- Carnegie Mellon University (US)
- ORCID
- Hovy, Eduard
- Venue
- Proceedings of the Workshop on Stylistic Variation
- Published
- 2017
- Pages
- 10-19
- OpenAlex
- W2732863878
- Cited by
- 156
- References
- 7
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 15 · 2022: 23 · 2021: 29 · 2020: 32 · 2019: 30 · 2018: 15 · 2017: 1
- FWCI
- 9.73
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Defense Advanced Research Projects Agency (FA8750, FA8750-12-2-0342)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- Shakespearean language, modern English, stylistic variation, text style transfer, copy mechanism, sequence-to-sequence models, pointer networks, word embeddings
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