GhostWriter: Using an LSTM for Automatic Rap Lyric Generation

Potash, Peter; Romanov, Alexey; Rumshisky, Anna

doi:10.18653/v1/d15-1221

Cited by 93 publications

(65 citation statements)

References 7 publications

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“…Much of this work has so far been published in the online blogosphere and the assessment of the quality of neurally generated text has often remained fairly informal and anecdotal, apart from a number of more empirically oriented studies, for instance in the field of hiphop lyric generation (Potash et al, 2015;Malmi et al, 2015). In this paper, we report an attempt at a systematic assessment of the properties of neurally generated text in the context of style-based authorship attribution in stylometry (Stamatatos, 2009).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution

Manjavacas¹,

Gussem²,

Daelemans³

et al. 2017

Proceedings of the Workshop on Stylistic Variation

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Recent applications of neural language models have led to an increased interest in the automatic generation of natural language. However impressive, the evaluation of neurally generated text has so far remained rather informal and anecdotal. Here, we present an attempt at the systematic assessment of one aspect of the quality of neurally generated text. We focus on a specific aspect of neural language generation: its ability to reproduce authorial writing styles. Using established models for authorship attribution, we empirically assess the stylistic qualities of neurally generated text. In comparison to conventional language models, neural models generate fuzzier text that is relatively harder to attribute correctly. Nevertheless, our results also suggest that neurally generated text offers more valuable perspectives for the augmentation of training data.

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Section: Introductionmentioning

confidence: 99%

Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution

Manjavacas¹,

Gussem²,

Daelemans³

et al. 2017

Proceedings of the Workshop on Stylistic Variation

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“…Beyond visual arts and music, applications to textual compositions have also emerged, not only in poetry (Wang et al, 2016;Yan, 2016) and literary creations (Roemmele, 2016) but also in rap song lyrics (Potash et al, 2015) and even in the production of screenplays, where "Benjamin" a LSTM-RNN "automatic screenwriter" created by Ross Goodwin and Oscar Sharp 9 wrote, autonomously, the sci-fi experimental short "Sunspring" (Newitz, 2016). Generative choreography (Antunes and Fol Leymarie, 2012;Crnkovic-Friis and Crnkovic-Friis, 2016) and creative productions of sculptures (Lehman et al, 2016) have also relied on the expressive potential of deep neural networks.…”

Section: The Spread Of Deep Creation In the Artsmentioning

confidence: 99%

Deep Creations: Intellectual Property and the Automata

Deltorn

2017

Front. Digit. Humanit.

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The rapid progress of deep neural network architectures is allowing both to automate the production of artworks and to extend the domain of creative expression. As such, it is opening new ground for professional and amateur artists alike. A major asset of these new computer processes is their capacity to derive, from a training phase, a generative model from which new artifacts can be produced. This attribute allows for a wide range of novel applications. New music or paintings in the style of famous artists can be produced at the click of a button, or combined to form new artworks. New graphical compositions can be "hallucinated" by the deep algorithmic models to produce striking, unexpected, visual forms. By the same token, the dependence on preexisting, protected, artworks lays the ground for potential zones of friction with the rights holders of the source data that helped shape the generative model. This articulation, between the popular creative movement initiated by the deep neural architectures and the preexisting rights of the authors, leads to a confrontation between the present legal framework for the protection of artistic creations and the new modalities offered by these new technological objects. The present work will address the conditions of protection of creations generated by deep neural networks under the main copyright regimes.

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“…These include Markov models (Barbieri et al, 2012) and models based on Deep Neural Networks (DNNs), including Recurrent Neural Networks (RNNs). Given a sequence of words, a RNN was used to predict the next word in rap lyrics (Potash et al, 2015). Or given the line history, RNNs can be used for generating new lines incrementally, considering their respective phonetics, structure and semantics (Zhang and Lapata, 2014;Yan, 2016).…”

Section: Artificial Intelligence Techniquesmentioning

confidence: 99%

A Survey on Intelligent Poetry Generation: Languages, Features, Techniques, Reutilisation and Evaluation

Oliveira¹

2017

Proceedings of the 10th International Conference on Natural Language Generation

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Poetry generation is becoming popular among researchers of Natural Language Generation, Computational Creativity and, broadly, Artificial Intelligence. To produce text that may be regarded as poetry, computational systems are typically knowledge-intensive and deal with several levels of language. Interest on the topic resulted in the development of several poetry generators described in the literature, with different features covered or handled differently, by a broad range of alternative approaches, as well as different perspectives on evaluation, another challenging aspect due the underlying subjectivity. This paper surveys intelligent poetry generators around a set of relevant axis -target language, form and content features, applied techniques, reutilisation of material, and evaluation -and aims to organise work developed on this topic so far.

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GhostWriter: Using an LSTM for Automatic Rap Lyric Generation

Cited by 93 publications

References 7 publications

Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution

Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution

Deep Creations: Intellectual Property and the Automata

A Survey on Intelligent Poetry Generation: Languages, Features, Techniques, Reutilisation and Evaluation

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