  Towards sufficient music searching, it is vital to form a complete set of
labels for each song. However, current solutions fail to resolve it as they
cannot produce diverse enough mappings to make up for the information missed by
the gold labels. Based on the observation that such missing information may
already be presented in user comments, we propose to study the automated music
labeling in an essential but under-explored setting, where the model is
required to harvest more diverse and valid labels from the users' comments
given limited gold labels. To this end, we design an iterative framework (DiVa)
to harvest more $\underline{\text{Di}}$verse and $\underline{\text{Va}}$lid
labels from user comments for music. The framework makes a classifier able to
form complete sets of labels for songs via pseudo-labels inferred from
pre-trained classifiers and a novel joint score function. The experiment on a
densely annotated testing set reveals the superiority of the Diva over
state-of-the-art solutions in producing more diverse labels missed by the gold
labels. We hope our work can inspire future research on automated music
labeling.
