We suggest a new NLG task in the context of the discourse generation pipeline of computational storytelling systems. This task, textual embellishment, is defined by taking a text as input and generating a semantically equivalent output with increased lexical and syntactic complexity. Ideally, this would allow the authors of computational storytellers to implement just lightweight NLG systems and use a domain-independent embellishment module to translate its output into more literary text. We present promising first results on this task using LSTM Encoder-Decoder networks trained on the WikiLarge dataset. Furthermore, we introduce "Compiled Computer Tales", a corpus of computationally generated stories, that can be used to test the capabilities of embellishment algorithms.
@article{arxiv.1810.08076,
title = {Discourse Embellishment Using a Deep Encoder-Decoder Network},
author = {Leonid Berov and Kai Standvoss},
journal= {arXiv preprint arXiv:1810.08076},
year = {2018}
}