MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling

Rui Mao, Kai He, Claudia Ong, Qian Liu, Erik Cambria

Abstract: Metaphor interpretation is a difficult task in natural language understanding. The development of relevant techniques in this domain is slow, mostly because of the lack of large annotated datasets and effective pre-trained language models (PLMs) for metaphor learning. Thus, we propose a large annotated dataset and a PLM for the metaphor interpretation task. Our foundation model is based on a novel anomalous language modeling (ALM) method, which we benchmark with comparable PLM baselines on the new dataset, finding that it largely improves model performance on metaphor identification and interpretation.