Neural Models of Factuality

Rachel Rudinger, Aaron Steven White, Benjamin Van Durme


Abstract
We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.
Anthology ID:
N18-1067
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
731–744
Language:
URL:
https://aclanthology.org/N18-1067
DOI:
10.18653/v1/N18-1067
Bibkey:
Cite (ACL):
Rachel Rudinger, Aaron Steven White, and Benjamin Van Durme. 2018. Neural Models of Factuality. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 731–744, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Neural Models of Factuality (Rudinger et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1067.pdf
Video:
 http://vimeo.com/276898126