Implicit Argument Prediction with Event Knowledge

Pengxiang Cheng, Katrin Erk


Abstract
Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract. Previous work has used models with large numbers of features, evaluated on very small datasets. We propose to train models for implicit argument prediction on a simple cloze task, for which data can be generated automatically at scale. This allows us to use a neural model, which draws on narrative coherence and entity salience for predictions. We show that our model has superior performance on both synthetic and natural data.
Anthology ID:
N18-1076
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:
831–840
Language:
URL:
https://www.aclweb.org/anthology/N18-1076
DOI:
10.18653/v1/N18-1076
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1076.pdf
Note:
 N18-1076.Notes.pdf