Papers by Cynthia Rudin

2 papers
The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation (2023.acl-short)

Copied to clipboard

Challenge: Rather than train a model to obey these constraints implicitly, we opt to enforce them explicitly using a simple but novel approach to generation.
Approach: They propose to automate the generation of sonnets within preset poetic constraints using a constrained decoding approach that uses a relatively modest neural backbone.
Outcome: The proposed method produces sonnets that adhere to the genre’s defined constraints and contain lyrical language and literary devices.
There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It (2021.tacl-1)

Copied to clipboard

Challenge: Existing algorithms for limerick generation are difficult to use, as they must follow strict structural, meter, and rhyming constraints.
Approach: They propose a system for automatic limerick generation that outperforms state-of-the-art models.
Outcome: The proposed system outperforms state-of-the-art models and rule-based models in generating limericks.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations