2017/07/04 by Harsh Jhamtani, Jhamtani, Harsh, Varun Gangal +5 · 4 citations
Arts and Humanities · Computer Science · #Computation and Language (cs.CL) #Digital Humanities and Scholarship #FOS: Computer and information sciences #Natural Language Processing Techniques #Video Analysis and Summarization #cs.CL
paper · pdf · doi:10.48550/arxiv.1707.01161
Accepted at EMNLP 2017 Workshop on Stylistic Variation
openalex publication_date 2017/07/04 · arxiv created 2017/07/20 · arxiv updated 2017/07/24 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
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.