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Interpreting Logical Metonymy through Dense Paraphrasing
Conference proceeding   Peer reviewed

Interpreting Logical Metonymy through Dense Paraphrasing

Bingyang Ye, Jingxuan Tu, Elisabetta Jezek and James Pustejovsky
Proceedings of the Annual Meeting of the Cognitive Science Society
Annual Meeting of the Cognitive Science Society, 44 (Toronto, CA, 07/27/2022–07/30/2022)
01/01/2022
Handle:
https://hdl.handle.net/10192/69996

Abstract

Artificial Intelligence Computational Modeling Linguistics Natural Language Processing Semantics of language
Compositionality has been argued to a necessary component of interpreting language, yet there appear to be many linguistic phenomena that do not overtly exhibit semantic compositional behavior. One of the challenges involves the phenomena of contextual modulations referred to collectively as semantic coercion or logical metonymy. In this paper, we present a computational model that provides the “compositional flexibility” in the interpretation of a verb with its arguments, for such coercive contexts in English. Specifically, we argue that such constructions typically have surface structural correlates in the form of dense paraphrases, and that these forms can be used to model the masked content in the coerced compositional context. We present preliminary results using a transformer architecture on a masked completion task. Our results show that modeling logical metonymy is a challenging task but can be substantially improved by fine-tuning through dense paraphrasing.
url
https://escholarship.org/uc/item/19k4w0c1View

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