Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models

Understand

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.