generated. A separate input of the content to be generated is provided in these cases.

External Feedback {#bg:sec-inp-ext}
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A regularizer is often used to control the external input ${\mathbf{h}}_0$ to the generator. In many cases, an adversarial loss to manipulate the latent space is used as an external feedback mechanism. This essentially controls the latent space of the encoder which is eventually provided as an initialization to the generator. In [@FuTan], a multi-layer perceptron (MLP) is used for predicting the style labels from ${\mathbf{h}}_0$. Similarly, the adversarial loss is also used in [@wang2019controllable] to control the latent representation ${\mathbf{h}}_0$ for style attributes. In [@romanov-etal-2019-adversarial], an adversarial loss is used to ensure that the meaning representation ${\mathbf{m}}$ does not carry any style signals. The adversarial loss is obtained by training a discriminator which takes as input a representation ${\mathbf{m}}$ and tells if it carries the target style signal. Similarly, this work also employs a motivator loss which is the opposite of the adversarial loss to ensure that the style representation ${\mathbf{f}}$ actually does carry the stylistic information. @john-etal-2019-disentangled use multiple losses to control the style and content information represented in ${\mathbf{h}}_0$.

The discriminator which provides external feedback has to be 