@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Generating Animations from Screenplays
%A Zhang, Yeyao
%A Tsipidi, Eleftheria
%A Schriber, Sasha
%A Kapadia, Mubbasir
%A Gross, Markus
%A Modi, Ashutosh
%Y Mihalcea, Rada
%Y Shutova, Ekaterina
%Y Ku, Lun-Wei
%Y Evang, Kilian
%Y Poria, Soujanya
%S Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F zhang-etal-2019-generating
%X 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.
%R 10.18653/v1/S19-1032
%U https://aclanthology.org/S19-1032
%U https://doi.org/10.18653/v1/S19-1032
%P 292-307
Markdown (Informal)
[Generating Animations from Screenplays](https://aclanthology.org/S19-1032) (Zhang et al., *SEM 2019)
ACL
- Yeyao Zhang, Eleftheria Tsipidi, Sasha Schriber, Mubbasir Kapadia, Markus Gross, and Ashutosh Modi. 2019. Generating Animations from Screenplays. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 292–307, Minneapolis, Minnesota. Association for Computational Linguistics.