Sensorimotor Enhanced Neural Network for Metaphor Detection

Mingyu Wan, Baixi Xing, Qi Su, Pengyuan Liu, Chu-Ren Huang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Detecting metaphors is challenging due to the subtle ontological differences between metaphorical and non-metaphorical expressions. Neural networks have been widely adopted in metaphor detection and become the main stream technology. However, linguistic insights have been less utilized. This work
proposes a linguistically enhanced model for metaphor detection extending one published work (WAN et al., 2020) by incorporating the modality norms into attention-based Bi-LSTM. Results show that the current model outperforms most recent works by 0.5%-11% F1, indicating the effectiveness of using modality norms for metaphor detection. This work provides a new perspective to detect token-level metaphoricity by leveraging the modality mismatch between words.
Original languageEnglish
Title of host publicationProceedings of the 34th Pacific Asia Conference on Language, Information and Computation
EditorsMinh Le Nguyen, Mai Chi Luong, Sanghoun Song
PublisherAssociation for Computational Linguistics (ACL)
Pages312–317
Publication statusPublished - Oct 2020
EventThe 34th Pacific Asia Conference on Language, Information and Computation (PACLIC-34) - Vietnam National University, Hanoi, Viet Nam
Duration: 24 Oct 202026 Oct 2020

Conference

ConferenceThe 34th Pacific Asia Conference on Language, Information and Computation (PACLIC-34)
Country/TerritoryViet Nam
CityHanoi
Period24/10/2026/10/20

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