You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Furthermore, we attempt to regularize the variational Transformer for controlling the chord outputs through a dis- entangled representation. Generating arbitrary sets of chords may not satisfy users who would like to create music based on their own tastes. In terms of building interactive music gen- eration systems as well as learning a good representation for sequential data, controllable generation with the VAE frame- work has mainly been approached by recent studies. These studies have aimed to learn disentangled representations for high-level musical features, such as pitch, rhythm, harmony, context, or arousal, through supervised learning [28]–[31]. Inspired by these studies, we use domain-speciﬁc induc- tive bias to achieve a disentangled representation for the well-summarized context of the target melody and chords. Furthermore, we compute Pearson’s correlation coefﬁ- cients between α and the CC scores of the corresponding chord outputs. Table 4 shows that rVTHarm reveals higher correlation coefﬁcients than VTHarm for all datasets. This conﬁrms that rVTHarm derives a meaningful representation for the intended chord attribute compared to VTHarm.

Question: Why can't training VTHarm guarantee a disentangled representation of the desired aspect?
VTHarm cannot guarantee a disentangled representation of the desired aspect because it does not aim a supervised learning that can decouple the representation by the high-level musical features. Empirically, the learned representation from VTHarm has been shown to be less correlated to the target chord attribute than rVTHarm which regularizes the representation.