Full metadata
Title
Event Detection as Multi-Task Text Generation
Description
Event detection refers to the task of identifying event occurrences in a given natural language text. Event detection comprises two subtasks; recognizing event mention (event identification) and the type of event (event classification). Breaking from the sequence labeling and word classification approaches, this work models event detection, and its constituent subtasks of trigger identification and trigger classification, as independent sequence generation tasks. This work proposes a prompted multi-task generative model trained on event identification, classification, and combined event detection. The model is evaluated on on general-domain and biomedical-domain event detection datasets, achieving state-of-the-art results on the general-domain Roles Across Multiple Sentences (RAMS) dataset, establishing event detection benchmark performance on WikiEvents, and achieving competitive performance on the general-domain Massive Event Detection (MAVEN) dataset and the biomedical-domain Multi-Level Event Extraction (MLEE) dataset.
Date Created
2022
Contributors
- Anantheswaran, Ujjwala (Author)
- Baral, Chitta (Thesis advisor)
- Kerner, Hannah (Committee member)
- Gopalan, Nakul (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
80 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.171580
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2022
Field of study: Computer Science
System Created
- 2022-12-20 12:33:10
System Modified
- 2022-12-20 12:52:47
- 1 year 10 months ago
Additional Formats