Abstract
We present a novel end-to-end generative task and system for predicting event factuality holders, targets, and their associated factuality values. We perform the first experiments using all sources and targets of factuality statements from the FactBank corpus. We perform multi-task learning with other tasks and event-factuality corpora to improve on the FactBank source and target task. We argue that careful domain specific target text output format in generative systems is important and verify this with multiple experiments on target text output structure. We redo previous state-of-the-art author-only event factuality experiments and also offer insights towards a generative paradigm for the author-only event factuality prediction task.- Anthology ID:
- 2023.findings-acl.44
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 701–715
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.44
- DOI:
- 10.18653/v1/2023.findings-acl.44
- Cite (ACL):
- John Murzaku, Tyler Osborne, Amittai Aviram, and Owen Rambow. 2023. Towards Generative Event Factuality Prediction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 701–715, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Towards Generative Event Factuality Prediction (Murzaku et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2023.findings-acl.44.pdf