Towards Generative Event Factuality Prediction

John Murzaku, Tyler G Osborne, Amittai F Aviram, Owen Rambow

Findings: Discourse and Pragmatics Findings Paper

Session 4: Discourse and Pragmatics (Virtual Poster)
Conference Room: Pier 7&8
Conference Time: July 11, 11:00-12:30 (EDT) (America/Toronto)
Global Time: July 11, Session 4 (15:00-16:30 UTC)
Spotlight Session: Spotlight - Metropolitan West (Spotlight)
Conference Room: Metropolitan West
Conference Time: July 10, 19:00-21:00 (EDT) (America/Toronto)
Global Time: July 10, Spotlight Session (23:00-01:00 UTC)
Keywords: discourse relations
TLDR: 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 oth...
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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.