Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text

Modeling semantic plausibility requires commonsense knowledge about the world and has been used as a testbed for exploring various knowledge representations. Previous work has focused specifically on modeling physical plausibility and shown that distributional methods fail when tested in a supervise...

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Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Porada, Ian, Suleman, Kaheer, Jackie Chi Kit Cheung
Format: Article
Language:English
Subjects:
Knowledge representation
Learning
Modelling
Semantics
Training
Voice recognition
Online Access:Get full text
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Summary:Modeling semantic plausibility requires commonsense knowledge about the world and has been used as a testbed for exploring various knowledge representations. Previous work has focused specifically on modeling physical plausibility and shown that distributional methods fail when tested in a supervised setting. At the same time, distributional models, namely large pretrained language models, have led to improved results for many natural language understanding tasks. In this work, we show that these pretrained language models are in fact effective at modeling physical plausibility in the supervised setting. We therefore present the more difficult problem of learning to model physical plausibility directly from text. We create a training set by extracting attested events from a large corpus, and we provide a baseline for training on these attested events in a self-supervised manner and testing on a physical plausibility task. We believe results could be further improved by injecting explicit commonsense knowledge into a distributional model.
ISSN:2331-8422
DOI:10.48550/arxiv.1911.05689