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Scripts are an important part of any TV series. They narrate movements,actions and expressions of characters. In this paper, a case study is presentedon how different sequence to sequence deep learning models perform in the taskof generating new conversations between characters as well as new scenarios onthe basis of a script (previous conversations). A comprehensive comparisonbetween these models, namely, LSTM, GRU and Bidirectional RNN is presented. Allthe models are designed to learn the sequence of recurring characters from theinput sequence. Each input sequence will contain, say "n" characters, and thecorresponding targets will contain the same number of characters, except, theywill be shifted one character to the right. In this manner, input and outputsequences are generated and used to train the models. A closer analysis ofexplored models performance and efficiency is delineated with the help of graphplots and generated texts by taking some input string. These graphs describeboth, intraneural performance and interneural model performance for each model.

LSTM vs. GRU vs. Bidirectional RNN for script generation