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dc.contributor.authorDavies, Craig
dc.contributor.authorWiratunga, Nirmalie
dc.contributor.authorMartin, Kyle
dc.date.accessioned2019-01-21T14:22:25Z
dc.date.available2019-01-21T14:22:25Z
dc.date.issued2018-11-16en
dc.identifier.citationDAVIES, C., WIRATUNGA, N. and MARTIN, K. 2018. GramError: a quality metric for machine generated songs. In Bramer, M. and PETRIDIS, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's specialist group on artificial intelligence (SGAI) annual international artificial intelligence conference (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 184-190. Available from: https://doi.org/10.1007/978-3-030-04191-5_16en
dc.identifier.isbn9783030041908en
dc.identifier.isbn9783030041915en
dc.identifier.urihttp://hdl.handle.net/10059/3271
dc.description.abstractThis paper explores whether a simple grammar-based metric can accurately predict human opinion of machine-generated song lyrics quality. The proposed metric considers the percentage of words written in natural English and the number of grammatical errors to rate the quality of machine-generated lyrics. We use a state-of-the-art Recurrent Neural Network (RNN) model and adapt it to lyric generation by re-training on the lyrics of 5,000 songs. For our initial user trial, we use a small sample of songs generated by the RNN to calibrate the metric. Songs selected on the basis of this metric are further evaluated using 'Turinglike' tests to establish whether there is a correlation between metric score and human judgment. Our results show that there is strong correlation with human opinion, especially at lower levels of song quality. They also show that 75% of the RNN-generated lyrics passed for human-generated over 30% of the time.en
dc.language.isoengen
dc.publisherSpringeren
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0en
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectNatural language generationen
dc.subjectQuality metricen
dc.subjectRecurrent neuralen
dc.subjectNetworken
dc.titleGramError: a quality metric for machine generated songs.en
dc.typeConference publicationsen
dc.publisher.urihttps://doi.org/10.1007/978-3-030-04191-5_16en
dcterms.dateAccepted2018-09-03en
dcterms.publicationdate2018-12-31en
refterms.accessExceptionNAen
refterms.dateDeposit2019-01-21en
refterms.dateEmbargoEnd2019-11-16en
refterms.dateFCA2019-11-16en
refterms.dateFreeToDownload2019-11-16en
refterms.dateFreeToRead2019-11-16en
refterms.dateToSearch2019-11-16en
refterms.depositExceptionNAen
refterms.panelBen
refterms.technicalExceptionNAen
refterms.versionAMen
rioxxterms.publicationdate2018-11-16en
rioxxterms.typeConference Paper/Proceeding/Abstracten
rioxxterms.versionAMen


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