parallelizing computation and a [*multi-headed*]{} attention which allows the model to jointly attend to information from different representation subspaces at different positions.

@sudhakar2019transforming use self-attention to control for style by simply adding a special target style token in the source sequence. @dinan2018wizard also use transformers to attend over information from external document for guided dialogue response generation. [@zhang2018personalizing] uses the encoded representation of personas to compute the attention weights ${\mathbf{a}}_t$ at a given time step of the decoder. The attention is re-weighted according to the persona of the response to be generated in dialogue. So far, work has not been done to modulate the attention weights to control for attributes like style, topic, content etc.

External Feedback {#bg:sec-out-ext}
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The output latent space of the generator can be controlled by external feedback. Similar to changing the external input ${\mathbf{h}}_0$, the output latent space can also be changed using adversarial loss. In [@logeswaran2018content], an adversarial loss is used which encourages the generation realistic and attribute compatible sentences. The adversarial loss tries to match the distribution of sentence and attribute vector pairs $({\mathbf{x}}, {\mathbf{s}})$ where the sentence can either be a real or generated sentence. @gong-etal-2019-reinforcement also control the output latent space 