Baruah, Sabyasachee; Narayanan, Shrikanth
Character Coreference Resolution in Movie Screenplays Inproceedings
In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 10300–10313, 2023.
Abstract | BibTeX | Tags: content analysis, coreference resolution, multimedia understanding
@inproceedings{baruah2023character,
title = {Character Coreference Resolution in Movie Screenplays},
author = {Sabyasachee Baruah and Shrikanth Narayanan},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
pages = {10300--10313},
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.},
keywords = {content analysis, coreference resolution, multimedia understanding},
pubstate = {published},
tppubtype = {inproceedings}
}
Baruah, Sabyasachee; Chakravarthula, Sandeep Nallan; Narayanan, Shrikanth
Annotation and Evaluation of Coreference Resolution in Screenplays Inproceedings
In: pp. 2004–2010, Association for Computational Linguistics, 2021.
Abstract | Links | BibTeX | Tags: coreference resolution
@inproceedings{baruah-etal-2021-annotation,
title = {Annotation and Evaluation of Coreference Resolution in Screenplays},
author = {Sabyasachee Baruah and Sandeep Nallan Chakravarthula and Shrikanth Narayanan},
doi = {10.18653/v1/2021.findings-acl.176},
year = {2021},
date = {2021-08-02},
pages = {2004–2010},
publisher = {Association for Computational Linguistics},
abstract = {Screenplays refer to characters using different names, pronouns, and nominal expressions. We need to resolve these mentions to
the correct referent character for better story understanding and holistic research in computational narratology. Coreference resolution of character mentions in screenplays becomes
challenging because of the large document lengths, unique structural features like scene headers, interleaving of action and speech
passages, and reliance on the accompanying video. In this work, we first adapt widely used annotation guidelines to address domain-specific issues in screenplays. We develop an automatic screenplay parser to extract the
structural information and design coreference rules based upon the structure. Our model exploits these structural features and outperforms a benchmark coreference model on the
screenplay coreference resolution task.},
keywords = {coreference resolution},
pubstate = {published},
tppubtype = {inproceedings}
}