Abstract
Distributional data tells us that a man can swallow candy, but not that a man can swallow a paintball, since this is never attested. However both are physically plausible events. This paper introduces the task of semantic plausibility: recognizing plausible but possibly novel events. We present a new crowdsourced dataset of semantic plausibility judgments of single events such as man swallow paintball. Simple models based on distributional representations perform poorly on this task, despite doing well on selection preference, but injecting manually elicited knowledge about entity properties provides a substantial performance boost. Our error analysis shows that our new dataset is a great testbed for semantic plausibility models: more sophisticated knowledge representation and propagation could address many of the remaining errors.- Anthology ID:
- N18-2049
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short 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:
- 303–308
- Language:
- URL:
- https://aclanthology.org/N18-2049
- DOI:
- 10.18653/v1/N18-2049
- Cite (ACL):
- Su Wang, Greg Durrett, and Katrin Erk. 2018. Modeling Semantic Plausibility by Injecting World Knowledge. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 303–308, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Modeling Semantic Plausibility by Injecting World Knowledge (Wang et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N18-2049.pdf
- Code
- suwangcompling/Modeling-Semantic-Plausibility-NAACL18