In the standard generation process, ${\mathbf{h}}_0$ is equal to ${\mathbf{h}}_e$. This is marked as module (1) in Figure \[fig:bg-overview\].

Arithmetic or Linear Transform {#sec:bg-dec-init-elem}
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One of the easiest ways to control the generation is to concatenate a control vector ${\mathbf{s}}$ to output of the encoder ${\mathbf{h}}_e$. The external input of the decoder ${\mathbf{h}}_0$ will be $[{\mathbf{h}}_e; {\mathbf{s}}]$, where $[a;b]$ denotes concatenation. Here, the control vector ${\mathbf{s}}$ would provide the generator with a strong signal to guide the generation process.

@fu:2017 use this technique to control the style representation for their generator. The encoder builds representation that is devoid of the style and only retains content. The control vector for style is then concatenated to the encoder representation to initialize the decoder. This technique is commonly used in [@ghazvininejad2018knowledge; @zhou2018dataset; @dinan2018wizard] to concatenate information from external sources to dialogue context to generate dialogue responses. @chandu2019my concatenate personality representation ${\mathcal{P}}$ derived from a separate corpus to generate visual stories. They also experiment with a simple arithmetic operation on ${\mathbf{h}}_e$ given by ${\mathbf{h}}_0 = {\mathbf{h}}_e - {\mathcal{S}} + {\mathcal{P}}$ to get the initialization of the generator (here ${\mathcal{S}}$ denotes the average representation of the story). They observed that while concatenation technique is 