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
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 831–840
- Language:
- URL:
- https://aclanthology.org/N18-1076
- DOI:
- 10.18653/v1/N18-1076
- Cite (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.
- Cite (Informal):
- Implicit Argument Prediction with Event Knowledge (Cheng & Erk, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/N18-1076.pdf
- Code
- pxch/event_imp_arg
- Data
- FrameNet, NomBank