Introducing Aspects of Creativity in Automatic Poetry Generation

• URL: http://arxiv.org/abs/2002.02511v1
• Date: Thu, 6 Feb 2020 20:44:12 GMT
• Title: Introducing Aspects of Creativity in Automatic Poetry Generation
• Authors: Brendan Bena and Jugal Kalita
• Abstract summary: Poetry Generation involves teaching systems to automatically generate text that resembles poetic work. A deep learning system can learn to generate poetry on its own by training on a corpus of poems and modeling the particular style of language. We propose taking an approach that fine-tunes GPT-2, a pre-trained language model, to our downstream task of poetry generation.
• Score: 2.792030485253753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Poetry Generation involves teaching systems to automatically generate text that resembles poetic work. A deep learning system can learn to generate poetry on its own by training on a corpus of poems and modeling the particular style of language. In this paper, we propose taking an approach that fine-tunes GPT-2, a pre-trained language model, to our downstream task of poetry generation. We extend prior work on poetry generation by introducing creative elements. Specifically, we generate poems that express emotion and elicit the same in readers, and poems that use the language of dreams---called dream poetry. We are able to produce poems that correctly elicit the emotions of sadness and joy 87.5 and 85 percent, respectively, of the time. We produce dreamlike poetry by training on a corpus of texts that describe dreams. Poems from this model are shown to capture elements of dream poetry with scores of no less than 3.2 on the Likert scale. We perform crowdsourced human-evaluation for all our poems. We also make use of the Coh-Metrix tool, outlining metrics we use to gauge the quality of text generated.

Related papers

• PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation [58.36105306993046]
  Controllable text generation is a challenging and meaningful field in natural language generation (NLG) In this paper, we pioneer the use of the Diffusion model for generating sonnets and Chinese SongCi poetry. Our model outperforms existing models in automatic evaluation of semantic, metrical, and overall performance as well as human evaluation.
  arXiv  Detail & Related papers  (2023-06-14T11:57:31Z)
• Generation of Chinese classical poetry based on pre-trained model [1.6114012813668934]
  This paper mainly tries to use BART and other pre training models to generate metrical poetry text. It developed a set of AI poetry Turing problems, it was reviewed by a group of poets and poetry writing researchers. The model of poetry generation studied by the author generalizes works that cannot be distinguished from those of advanced scholars.
  arXiv  Detail & Related papers  (2022-11-04T16:05:31Z)
• PoeticTTS -- Controllable Poetry Reading for Literary Studies [21.29478270833139]
  We resynthesise poems by cloning prosodic values from a human reference recitation, and afterwards make use of fine-grained prosody control to manipulate the synthetic speech. We find that finetuning our TTS model on poetry captures poetic intonation patterns to a large extent which is beneficial for prosody cloning and manipulation.
  arXiv  Detail & Related papers  (2022-07-11T13:15:27Z)
• PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation [42.12348554537587]
  Formal verse poetry imposes strict constraints on the meter and rhyme scheme of poems. Most prior work on generating this type of poetry uses existing poems for supervision. We propose an unsupervised approach to generate poems following any given meter and rhyme scheme.
  arXiv  Detail & Related papers  (2022-05-24T17:09:55Z)
• Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features [37.45490765899826]
  We present a novel framework to generate sonnets that does not require training on poems. Specifically, a content planning module is trained on non-poetic texts to obtain discourse-level coherence. We also design a constrained decoding algorithm to impose the meter-and-rhyme constraint of the generated sonnets.
  arXiv  Detail & Related papers  (2022-05-03T23:44:28Z)
• CCPM: A Chinese Classical Poetry Matching Dataset [50.90794811956129]
  We propose a novel task to assess a model's semantic understanding of poetry by poem matching. This task requires the model to select one line of Chinese classical poetry among four candidates according to the modern Chinese translation of a line of poetry. To construct this dataset, we first obtain a set of parallel data of Chinese classical poetry and modern Chinese translation.
  arXiv  Detail & Related papers  (2021-06-03T16:49:03Z)
• Generate and Revise: Reinforcement Learning in Neural Poetry [17.128639251861784]
  We propose a framework to generate poems that are repeatedly revisited and corrected, as humans do, in order to improve their overall quality. Our model generates poems from scratch and it learns to progressively adjust the generated text in order to match a target criterion. We evaluate this approach in the case of matching a rhyming scheme, without having any information on which words are responsible of creating rhymes and on how to coherently alter the poem words.
  arXiv  Detail & Related papers  (2021-02-08T10:35:33Z)
• Acrostic Poem Generation [26.604889384391726]
  We propose a new task in the area of computational creativity: acrostic poem generation in English. Acrostic poems are poems that contain a hidden message; typically, the first letter of each line spells out a word or short phrase. Our experiments show that the acrostic poems generated by our baseline are received well by humans and do not lose much quality due to the additional constraints.
  arXiv  Detail & Related papers  (2020-10-05T18:00:15Z)
• Generating Major Types of Chinese Classical Poetry in a Uniformed Framework [88.57587722069239]
  We propose a GPT-2 based framework for generating major types of Chinese classical poems. Preliminary results show this enhanced model can generate Chinese classical poems of major types with high quality in both form and content.
  arXiv  Detail & Related papers  (2020-03-13T14:16:25Z)
• MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space [79.70053419040902]
  We propose MixPoet, a novel model that absorbs multiple factors to create various styles and promote diversity. Based on a semi-supervised variational autoencoder, our model disentangles the latent space into some subspaces, with each conditioned on one influence factor by adversarial training. Experiment results on Chinese poetry demonstrate that MixPoet improves both diversity and quality against three state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-13T03:31:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.