  GANs have been shown to perform exceedingly well on tasks pertaining to image
generation and style transfer. In the field of language modelling, word
embeddings such as GLoVe and word2vec are state-of-the-art methods for applying
neural network models on textual data. Attempts have been made to utilize GANs
with word embeddings for text generation. This study presents an approach to
text generation using Skip-Thought sentence embeddings with GANs based on
gradient penalty functions and f-measures. The proposed architecture aims to
reproduce writing style in the generated text by modelling the way of
expression at a sentence level across all the works of an author. Extensive
experiments were run in different embedding settings on a variety of tasks
including conditional text generation and language generation. The model
outperforms baseline text generation networks across several automated
evaluation metrics like BLEU-n, METEOR and ROUGE. Further, wide applicability
and effectiveness in real life tasks are demonstrated through human judgement
scores.
