mlsanityData to 7 Oct 2026

GPoeT-2: A GPT-2 Based Poem Generator

arXiv 2205.08847 ↗First posted 18 May 2022cs.CL cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

This project aims to produce the next volume of machine-generated poetry, a complex art form that can be structured and unstructured, and carries depth in the meaning between the lines. GPoeT-2 is based on fine-tuning a state of the art natural language model (i.e. GPT-2) to generate limericks, typically humorous structured poems consisting of five lines with a AABBA rhyming scheme. With a two-stage generation system utilizing both forward and reverse language modeling, GPoeT-2 is capable of freely generating limericks in diverse topics while following the rhyming structure without any seed phrase or a posteriori constraints.Based on the automated generation process, we explore a wide variety of evaluation metrics to quantify "good poetry," including syntactical correctness, lexical diversity, and subject continuity. Finally, we present a collection of 94 categorized limericks that rank highly on the explored "good poetry" metrics to provoke human creativity.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 18 May 2022.

Builds on

13 of 24 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  4. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  5. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  6. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  7. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  8. 2019BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Modelcited by 184
  9. 2019Leveraging Pre-trained Checkpoints for Sequence Generation Taskscited by 182
  10. 2020Deep Learning Based Text Classification: A Comprehensive Reviewcited by 140
  11. 2020Learning Autocompletion from Real-World Datasetscited by 19
  12. 2020Introducing Aspects of Creativity in Automatic Poetry Generationcited by 14
1 more
  1. 2019Learning Rhyming Constraints using Structured Adversariescited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 1.

5 papers.