MHI Research Festival @ USC, Los Angeles

Presenting my poster at MHI Research Festival, USC

Presenting my poster at MHI Research Festival at USC

I presented the poster of my work on character coreference resolution at the Ming Hsieh Institute Research Festival on Oct 27, 2023.

Check out the project page.

References

2023

Character Coreference Resolution in Movie Screenplays

Sabyasachee Baruah, and Shrikanth Narayanan

In Findings of the Association for Computational Linguistics: ACL 2023, Jul 2023

Abs Bib HTML

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.
@inproceedings{baruah-narayanan-2023-character, title = {Character Coreference Resolution in Movie Screenplays}, author = {Baruah, Sabyasachee and Narayanan, Shrikanth}, booktitle = {Findings of the Association for Computational Linguistics: ACL 2023}, month = jul, year = {2023}, address = {Toronto, Canada}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2023.findings-acl.654}, doi = {10.18653/v1/2023.findings-acl.654}, pages = {10300--10313}, }

2021

Annotation and Evaluation of Coreference Resolution in Screenplays

Sabyasachee Baruah, Sandeep Nallan Chakravarthula, and Shrikanth Narayanan

In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Aug 2021

Abs Bib HTML

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.
@inproceedings{baruah-etal-2021-annotation, title = {Annotation and Evaluation of Coreference Resolution in Screenplays}, author = {Baruah, Sabyasachee and Nallan Chakravarthula, Sandeep and Narayanan, Shrikanth}, booktitle = {Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021}, month = aug, year = {2021}, address = {Online}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2021.findings-acl.176}, doi = {10.18653/v1/2021.findings-acl.176}, pages = {2004--2010}, }