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        <title>Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text</title>
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    <name type="personal">
        <namePart type="given">Ian</namePart>
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    <name type="personal">
        <namePart type="given">Kaheer</namePart>
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        <namePart type="given">Kit</namePart>
        <namePart type="family">Cheung</namePart>
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            <title>Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing</title>
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            <namePart type="given">Simon</namePart>
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            <namePart type="given">Sheng</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/D19-6015</identifier>
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%0 Conference Proceedings
%T Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text
%A Porada, Ian
%A Suleman, Kaheer
%A Cheung, Jackie Chi Kit
%Y Ostermann, Simon
%Y Zhang, Sheng
%Y Roth, Michael
%Y Clark, Peter
%S Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F porada-etal-2019-gorilla
%X 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.
%R 10.18653/v1/D19-6015
%U https://aclanthology.org/D19-6015
%U https://doi.org/10.18653/v1/D19-6015
%P 123-129

Markdown (Informal)

[Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text](https://aclanthology.org/D19-6015) (Porada et al., 2019)

• Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text (Porada et al., 2019)

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.