${\mathbf{s}}$ of the word to be defined at each time step of the definition generation process. Unfortunately, for this task, this technique has not proved to be effective compared to other techniques of controlling the generation. @zhou2018dataset concatenate the hidden representation of the external source of information ${\mathbf{s}}$ to each time step of dialogue response generation. Similarly, @prabhumoye-etal-2019-towards also concatenate the hidden representation of the external source of information $s$ to each time step of Wikipedia update generation process. In this work as well, this results of this technique were not as impressive as simple concatenating the control context to the input of the encoder. @harrison2019maximizing concatenate a side constraint $s$ which represents style and personality into the generation process. For this task of generating language from meaning representations with stylistic variation, this method performed better than conditioning the encoder with side constraint in terms of BLEU metric. @chandu2019my also concatenate the personality representation ${\mathcal{P}}$ at each time step of the story generation process. This is used to control the personality of the visual stories. In addition to concatenation, this work proposes to modify the sequential input as ${\mathbf{\tilde{x}}}_t = {\mathbf{x}}_t - {\mathcal{S}} + {\mathcal{P}}$ (here ${\mathcal{S}}$ denotes the average 