mlsanityData to 14 Oct 2026

BACON: Deep-Learning Powered AI for Poetry Generation with Author Linguistic Style Transfer

arXiv 2112.11483 ↗First posted 14 Dec 2021cs.CL cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

This paper describes BACON, a basic prototype of an automatic poetry generator with author linguistic style transfer. It combines concepts and techniques from finite state machinery, probabilistic models, artificial neural networks and deep learning, to write original poetry with rich aesthetic-qualities in the style of any given author. Extrinsic evaluation of the output generated by BACON shows that participants were unable to tell the difference between human and AI-generated poems in any statistically significant way.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 14 Dec 2021.

Builds on

9 of 32 references.

References · 9

  1. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  2. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  3. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
  4. 2014Deep Learning in Neural Networks: An Overviewcited by 770
  5. 2015A Neural Algorithm of Artistic Stylecited by 483
  6. 2015DRAW: A Recurrent Neural Network For Image Generationcited by 191
  7. 2017Controlling Linguistic Style Aspects in Neural Language Generationcited by 149
  8. 2017Discovering Discrete Latent Topics with Neural Variational Inferencecited by 79
  9. 2016Investigating gated recurrent neural networks for speech synthesiscited by 3

Built on by

Not cited yet.