Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models

Harsh Jhamtani, Varun Gangal, Eduard Hovy, Eric Nyberg


Abstract
Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose. However, applying stylistic variations is still by and large 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 above the strongest baseline. We publicly release our code to foster further research in this area.
Anthology ID:
W17-4902
Volume:
Proceedings of the Workshop on Stylistic Variation
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Julian Brooke, Thamar Solorio, Moshe Koppel
Venue:
Style-Var
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10–19
Language:
URL:
https://aclanthology.org/W17-4902
DOI:
10.18653/v1/W17-4902
Bibkey:
Cite (ACL):
Harsh Jhamtani, Varun Gangal, Eduard Hovy, and Eric Nyberg. 2017. Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models. In Proceedings of the Workshop on Stylistic Variation, pages 10–19, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models (Jhamtani et al., Style-Var 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-4902.pdf
Attachment:
 W17-4902.Attachment.zip
Code
 harsh19/Shakespearizing-Modern-English