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

Ian Porada, Kaheer Suleman, Jackie Chi Kit Cheung


Abstract
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.
Anthology ID:
D19-6015
Volume:
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
123–129
Language:
URL:
https://aclanthology.org/D19-6015
DOI:
10.18653/v1/D19-6015
Bibkey:
Cite (ACL):
Ian Porada, Kaheer Suleman, and Jackie Chi Kit Cheung. 2019. Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text. In Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing, pages 123–129, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text (Porada et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-6015.pdf