  Controlled text generation is a very important task in the arena of natural
language processing due to its promising applications. In order to achieve this
task we mainly introduce the novel soft prompt tuning method of using soft
prompts at both encoder and decoder levels together in a T5 model and
investigate the performance as the behaviour of an additional soft prompt
related to the decoder of a T5 model in controlled text generation remained
unexplored. Then we also investigate the feasibility of steering the output of
this extended soft prompted T5 model at decoder level and finally analyse the
utility of generated text to be used in AI related tasks such as training AI
models with an interpretability analysis of the classifier trained with
synthetic text, as there is a lack of proper analysis of methodologies in
generating properly labelled data to be utilized in AI tasks. Through the
performed in-depth intrinsic and extrinsic evaluations of this generation model
along with the artificially generated data, we found that this model produced
better results compared to the T5 model with a single soft prompt at encoder
level and the sentiment classifier trained using this artificially generated
data can produce comparable classification results to the results of a
classifier trained with real labelled data and also the classifier decision is
interpretable with respect to the input text content.
