Year: 2018
Type: conference-paper
Abstract: 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 Wiki-Large dataset.Furthermore, we introduce "Compiled Computer Tales", a corpus of computationally generated stories, that can be used to test the capabilities of embellishment algorithms.
DOI: 10.18653/v1/w18-6603
Authors: Leonid Berov, Kai Standvoss
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2962909609