Romantic-Computing
Not on the map. Why?
Abstract (arXiv)
In this paper we compare various text generation models' ability to write poetry in the style of early English Romanticism. These models include: Character-Level Recurrent Neural Networks with Long Short-Term Memory, Hugging Face's GPT-2, OpenAI's GPT-3, and EleutherAI's GPT-NEO. Quality was measured based syllable count and coherence with the automatic evaluation metric GRUEN. Character-Level Recurrent Neural Networks performed far worse compared to transformer models. And, as parameter-size increased, the quality of transformer models' poems improved. These models are typically not compared in a creative context, and we are happy to contribute.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 14 references that are in the map. First posted on arXiv 1 Jun 2022.
Builds on
7 of 14 references.
References · 7
- 2017Attention Is All You Needcited by 60,361
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
- 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
- 2020GRUEN for Evaluating Linguistic Quality of Generated Textcited by 37
- 2018Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhymecited by 35
- 2021Generate and Revise: Reinforcement Learning in Neural Poetrycited by 3
Built on by
Not cited yet.