Conference Paper Published

EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information

Li, Y., Peng, B., Hsu, Y. Y., & Huang, C. R. (2024). EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information. In Findings of the Association for Computational Linguistics: EMNLP 2024, 16868-16876.

DOI: https://doi.org/10.18653/v1/2024.findings-emnlp.982

Abstract

The identification of metaphor is a crucial prerequisite for many downstream language tasks, such as sentiment analysis, opinion mining, and textual entailment. State-of-the-art systems of metaphor detection implement heuristic principles such as Metaphor Identification Procedure (MIP) (Pragglejaz Group, 2007) and Selection Preference Violation (SPV) (Wilks, 1975; Wilson, 2002). We propose an innovative approach that leverages the cognitive information of embodiment that can be derived from word embeddings, and explicitly models the process of sensorimotor change that has been demonstrated as essential for human metaphor processing. We showed that this cognitively motivated module is effective and can improve metaphor detection, compared with the heuristic MIP that has been applied previously.