@inproceedings{murzaku-etal-2023-towards,
title = "Towards Generative Event Factuality Prediction",
author = "Murzaku, John and
Osborne, Tyler and
Aviram, Amittai and
Rambow, Owen",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
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.44",
doi = "10.18653/v1/2023.findings-acl.44",
pages = "701--715",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Towards Generative Event Factuality Prediction
%A Murzaku, John
%A Osborne, Tyler
%A Aviram, Amittai
%A Rambow, Owen
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F murzaku-etal-2023-towards
%X 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.
%R 10.18653/v1/2023.findings-acl.44
%U https://aclanthology.org/2023.findings-acl.44
%U https://doi.org/10.18653/v1/2023.findings-acl.44
%P 701-715
Markdown (Informal)
[Towards Generative Event Factuality Prediction](https://aclanthology.org/2023.findings-acl.44) (Murzaku et al., Findings 2023)
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.