representation of the story and ${\mathcal{P}}$ denotes the representation of the personality). The latter technique is better at generating personality conditioned stories than the concatenation technique. Neither of these techniques prove to be conclusively better than making similar changes to the external input module ([§\[sec:bg-dec-init-elem\]]{}). Note that in this technique, changes are made directly to the input of generation and not the context which is the case with external input. Also, most of the prior work has focused on recurrent neural network and its variants for making such changes. It could be interesting to see such changes made to transformers [@vaswani2017attention].

Generator Operations {#sec:bg-gen}
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This module takes in the external input ${\mathbf{h}}_0$, the sequential input ${\mathbf{x}}_t$ at time step $t$ and performs computation to return an output ${\mathbf{o}}_t$. The same set of computations ($\boldsymbol{G}$) are performed at each time step. Different set of operations can be performed to compute ${\mathbf{o}}_t$ which are enlisted below. You can also decide to change the operations based on the control vector ${\mathbf{s}}$ to compute ${\mathbf{o}}_t$. This is shown as position (3) in Figure \[fig:bg-overview\].

Recurrent Neural Networks {#sec:bg-gen-rnn}
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Recurrent Neural Networks (RNNs) are designed to model sequential information. RNNs perform the same 