Abstract

Movie screenplays have a distinct narrative structure. It segments the story into scenes containing interleaving descriptions of actions, locations, and character dialogues. A typical screenplay spans several scenes and can include long-range dependencies between characters and events. A holistic document-level understanding of the screenplay requires several natural language processing capabilities, such as parsing, character identification, coreference resolution, action recognition, summarization, and attribute discovery. In this work, we develop scalable and robust methods to extract the structural information and character coreference clusters from full-length movie screenplays. We curate two datasets for screenplay parsing and character coreference—MovieParse and MovieCoref, respectively. We build a robust screenplay parser to handle inconsistencies in screenplay formatting and leverage the parsed output to link co-referring character mentions. Our coreference models can scale to long screenplay documents without drastically increasing their memory footprints.

Metadata

publication

Findings of the Association for Computational Linguistics: ACL 2023, 10300-10313, 2023

year

2023

publication date

2023/7

authors

Sabyasachee Baruah, Shrikanth Narayanan

link

https://aclanthology.org/2023.findings-acl.654/

resource_link

https://aclanthology.org/2023.findings-acl.654.pdf

conference

Findings of the Association for Computational Linguistics: ACL 2023

pages

10300-10313