John Murzaku


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Towards Generative Event Factuality Prediction
John Murzaku | Tyler Osborne | Amittai Aviram | Owen Rambow
Findings of the Association for Computational Linguistics: ACL 2023

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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Re-Examining FactBank: Predicting the Author’s Presentation of Factuality
John Murzaku | Peter Zeng | Magdalena Markowska | Owen Rambow
Proceedings of the 29th International Conference on Computational Linguistics

We present a corrected version of a subset of the FactBank data set. Previously published results on FactBank are no longer valid. We perform experiments on FactBank using multiple training paradigms, data smoothing techniques, and polarity classifiers. We argue that f-measure is an important alternative evaluation metric for factuality. We provide new state-of-the-art results for four corpora including FactBank. We perform an error analysis on Factbank combined with two similar corpora.