most citedShakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CL2019

Learning Rhyming Constraints using Structured Adversaries

Harsh Jhamtani, Sanket Vaibhav Mehta, Jaime Carbonell +1

Existing recurrent neural language models often fail to capture higher-level structure present in text: for example, rhyming patterns present in poetry. Much prior work on poetry g…

cs.CL2017

SPINE: SParse Interpretable Neural Embeddings

Anant Subramanian, Danish Pruthi, Harsh Jhamtani +2

Prediction without justification has limited utility. Much of the success of neural models can be attributed to their ability to learn rich, dense and expressive representations. W…

cs.CL2017

CharManteau: Character Embedding Models For Portmanteau Creation

Varun Gangal, Harsh Jhamtani, Graham Neubig +2

Portmanteaus are a word formation phenomenon where two words are combined to form a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of p…

cs.CL20171 cited

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

Harsh Jhamtani, Varun Gangal, Eduard Hovy +1

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 manu…

cs.CL2017

Generating Appealing Brand Names

Gaurush Hiranandani, Pranav Maneriker, Harsh Jhamtani

Providing appealing brand names to newly launched products, newly formed companies or for renaming existing companies is highly important as it can play a crucial role in deciding…