Modeling Semantic Plausibility by Injecting World Knowledge

ACL 2018

Modeling Semantic Plausibility by Injecting World Knowledge

Jan 21, 2021
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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. Authors: Su Wang, Greg Durrett, Katrin Erk (The University of Texas at Austin)

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