A Robot’s Street Credibility: Modeling authenticity judgments for artificially generated Hip-Hop lyrics

Enrique Manjavacas, Mike Kestemont, F.B. Karsdorp

Research output: Chapter in book/volumeContribution to conference proceedingsScientificpeer-review

Abstract

This study aims to advance and enhance our understanding of the properties that contribute to the perceived authenticity of a specific art form: Hip-Hop lyrics. The basis of our study is an experiment carried out in the context of a large, mainstream contemporary music festival. We crowdsourced a large dataset of authenticity judgements for both authentic and neurally generated Hip-Hop lyrics, which enable us to quantitatively assess human biases toward artificially generated text as well as which linguistic characteristics are perceived as authenticity cues. Additionally, the dataset provides solid ground for evaluating different neural language generation systems with respect to their perceived credibility. We compare a variety of language models and techniques. Our experiments contribute equally to improving the credibility of generated text and enhancing our understanding of the cognitive processes at play in the perception of authentic and artificial art.
Original languageEnglish
Title of host publicationProceedings of the 2019 Digital Humanities conference
Publication statusPublished - 2019

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