@InProceedings{zhang-EtAl:2019:S19-1,
  author    = {Zhang, Yeyao  and  Tsipidi, Eleftheria  and  Schriber, Sasha  and  Kapadia, Mubbasir  and  Gross, Markus  and  Modi, Ashutosh},
  title     = {Generating Animations from Screenplays},
  booktitle = {Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota},
  publisher = {Association for Computational Linguistics},
  pages     = {292--307},
  abstract  = {Automatically generating animation from natural language text finds application in a number of areas e.g. movie script writing, instructional videos, and public safety. However, translating natural language text into animation is a challenging task. Existing text-to-animation systems can handle only very simple sentences, which limits their applications. In this paper, we develop a text-to-animation system which is capable of handling complex sentences. We achieve this by introducing a text simplification step into the process. Building on an existing animation generation system for screenwriting, we create a robust NLP pipeline to extract information from screenplays and map them to the system's knowledge base. We develop a set of linguistic transformation rules that simplify complex sentences. Information extracted from the simplified sentences is used to generate a rough storyboard and video depicting the text. Our sentence simplification module outperforms existing systems in terms of BLEU and SARI metrics.We further evaluated our system via a user study: 68\% participants believe that our system generates reasonable animation from input screenplays.},
  url       = {http://www.aclweb.org/anthology/S19-1032}
}