be potentially used for applying these techniques on new tasks of similar nature. It also provides an easy access to appropriate comparison with existing techniques for those new architectures. The prior work on unifying text generation models has mostly focused on building efficient tool-kits and modular views of generation. For instance, [@reiter2000buildNLG] details seven sub-tasks which are conceptually distinct to describe the generation process. These sub-tasks can be modelled separately or in some cases they may interleave. In [@reiter2000buildNLG], these seven sub-tasks are primarily characterized as content or structure tasks. Note that this work is not specific to neural text generation. Our work focuses specifically on controlling attributes in neural text generation process. We don’t divide the generation pipeline into several sub-tasks but we divide the neural text generation process into modules all of which are required for generation. In [@hu2019texar], the focus is on building a toolkit for various text generation tasks based on the three properties of versatility, modularity and extensibility. This work enlists few model architectures and learning paradigms for various text generation tasks. In our work, we focus only on the generation process of controllable text generation tasks. We specifically detail the inputs, outputs and operations 