@inproceedings{cheng-erk-2018-implicit,
title = "Implicit Argument Prediction with Event Knowledge",
author = "Cheng, Pengxiang and
Erk, Katrin",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1076",
doi = "10.18653/v1/N18-1076",
pages = "831--840",
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.",
}
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%0 Conference Proceedings
%T Implicit Argument Prediction with Event Knowledge
%A Cheng, Pengxiang
%A Erk, Katrin
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F cheng-erk-2018-implicit
%X 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.
%R 10.18653/v1/N18-1076
%U https://aclanthology.org/N18-1076
%U https://doi.org/10.18653/v1/N18-1076
%P 831-840
Markdown (Informal)
[Implicit Argument Prediction with Event Knowledge](https://aclanthology.org/N18-1076) (Cheng & Erk, NAACL 2018)
ACL
- Pengxiang Cheng and Katrin Erk. 2018. Implicit Argument Prediction with Event Knowledge. 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 831–840, New Orleans, Louisiana. Association for Computational Linguistics.