the form vector ${\mathbf{f}}$. This work shows qualitatively how ${\mathbf{m}}$ and ${\mathbf{f}}$ are learnt in the subspaces using t-SNE plots. It also shows quantitatively the use of ${\mathbf{m}}$ and ${\mathbf{f}}$ in downstream paraphrase detection tasks. This is an excellent method in building interpretable representations for control attributes. Although, the effectiveness of this technique is not yet proven in the style transfer task or the abstractive summarization task. In both the above mentioned works, the models learns interpretable representations of control attributes but were not able to beat state of the art methods in their respective tasks. It is also worth noting that learning good decomposed vectors is especially hard when no supervision is provided on what the decomposed components are supposed to learn.

This techniques works well when the representation space of the input ${\mathbf{x}}$ can be decomposed into subspaces which can represent the control attributes. This means that the input ${\mathbf{x}}$ needs to contain signal of the control attributes. It is unlikely to work when the control attributes need to be externally provided. For example in case of content grounded generation tasks described in [@prabhumoye-etal-2019-towards; @dinan2018wizard; @zhou2018dataset], the input may not necessarily contain the content that needs to be 