  Scripts are an important part of any TV series. They narrate movements,
actions and expressions of characters. In this paper, a case study is presented
on how different sequence to sequence deep learning models perform in the task
of generating new conversations between characters as well as new scenarios on
the basis of a script (previous conversations). A comprehensive comparison
between these models, namely, LSTM, GRU and Bidirectional RNN is presented. All
the models are designed to learn the sequence of recurring characters from the
input sequence. Each input sequence will contain, say "n" characters, and the
corresponding targets will contain the same number of characters, except, they
will be shifted one character to the right. In this manner, input and output
sequences are generated and used to train the models. A closer analysis of
explored models performance and efficiency is delineated with the help of graph
plots and generated texts by taking some input string. These graphs describe
both, intraneural performance and interneural model performance for each model.
