@inproceedings{zhang-etal-2019-generating,
    title = "Generating Animations from Screenplays",
    author = "Zhang, Yeyao  and
      Tsipidi, Eleftheria  and
      Schriber, Sasha  and
      Kapadia, Mubbasir  and
      Gross, Markus  and
      Modi, Ashutosh",
    editor = "Mihalcea, Rada  and
      Shutova, Ekaterina  and
      Ku, Lun-Wei  and
      Evang, Kilian  and
      Poria, Soujanya",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/S19-1032/",
    doi = "10.18653/v1/S19-1032",
    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."
}