# There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It

arXiv: https://arxiv.org/abs/2103.03775 · HTML: https://mlsanity.com/paper/2103.03775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://mlsanity.com/paper/1804.06609/) (2018)
- [Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge](https://mlsanity.com/paper/1609.06647/) (2016)
- [Guided Open Vocabulary Image Captioning with Constrained Beam Search](https://mlsanity.com/paper/1612.00576/) (2016)
- [Learning Neural Templates for Text Generation](https://mlsanity.com/paper/1808.10122/) (2018)
- [Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech](https://mlsanity.com/paper/1805.12589/) (2018)
- [Mutual Information and Diverse Decoding Improve Neural Machine Translation](https://mlsanity.com/paper/1601.00372/) (2016)
- [Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme](https://mlsanity.com/paper/1807.03491/) (2018)
- [Chinese Poetry Generation with Planning based Neural Network](https://mlsanity.com/paper/1610.09889/) (2016)
- [Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training](https://mlsanity.com/paper/1804.08473/) (2018)
- [MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space](https://mlsanity.com/paper/2003.06094/) (2020)
- [BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization](https://mlsanity.com/paper/1906.05012/) (2019)
- [Chinese Poetry Generation with a Working Memory Model](https://mlsanity.com/paper/1809.04306/) (2018)
- [Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation](https://mlsanity.com/paper/1811.05067/) (2018)

## Built on by (most cited first, 1 of 1)

- [Creative Data Generation: A Review Focusing on Text and Poetry](https://mlsanity.com/paper/2305.08493/) (2023)

## Abstract (arXiv)

Limerick generation exemplifies some of the most difficult challenges faced in poetry generation, as the poems must tell a story in only five lines, with constraints on rhyme, stress, and meter. To address these challenges, we introduce LimGen, a novel and fully automated system for limerick generation that outperforms state-of-the-art neural network-based poetry models, as well as prior rule-based poetry models. LimGen consists of three important pieces: the Adaptive Multi-Templated Constraint algorithm that constrains our search to the space of realistic poems, the Multi-Templated Beam Search algorithm which searches efficiently through the space, and the probabilistic Storyline algorithm that provides coherent storylines related to a user-provided prompt word. The resulting limericks satisfy poetic constraints and have thematically coherent storylines, which are sometimes even funny (when we are lucky).