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            <namePart type="given">Regina</namePart>
            <namePart type="family">Barzilay</namePart>
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    <abstract>Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, “Tyler entered his house” implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.</abstract>
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%0 Conference Proceedings
%T Verb Physics: Relative Physical Knowledge of Actions and Objects
%A Forbes, Maxwell
%A Choi, Yejin
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F forbes-choi-2017-verb
%X Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, “Tyler entered his house” implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.
%R 10.18653/v1/P17-1025
%U https://aclanthology.org/P17-1025
%U https://doi.org/10.18653/v1/P17-1025
%P 266-276

Markdown (Informal)

[Verb Physics: Relative Physical Knowledge of Actions and Objects](https://aclanthology.org/P17-1025) (Forbes & Choi, ACL 2017)

• Verb Physics: Relative Physical Knowledge of Actions and Objects (Forbes & Choi, ACL 2017)

ACL

• Maxwell Forbes and Yejin Choi. 2017. Verb Physics: Relative Physical Knowledge of Actions and Objects. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 266–276, Vancouver, Canada. Association for Computational Linguistics.