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Towards sufficient music searching, it is vital to form a complete set oflabels for each song. However, current solutions fail to resolve it as theycannot produce diverse enough mappings to make up for the information missed bythe gold labels. Based on the observation that such missing information mayalready be presented in user comments, we propose to study the automated musiclabeling in an essential but under-explored setting, where the model isrequired to harvest more diverse and valid labels from the users' commentsgiven limited gold labels. To this end, we design an iterative framework (DiVa)to harvest more $\underline{\text{Di}}$verse and $\underline{\text{Va}}$lidlabels from user comments for music. The framework makes a classifier able toform complete sets of labels for songs via pseudo-labels inferred frompre-trained classifiers and a novel joint score function. The experiment on adensely annotated testing set reveals the superiority of the Diva overstate-of-the-art solutions in producing more diverse labels missed by the goldlabels. We hope our work can inspire future research on automated musiclabeling.

DiVa: An Iterative Framework to Harvest More Diverse and Valid Labels from User Comments for Music