Modality Enriched Neural Network for Metaphor Detection

Abstract

Metaphor as a cognitive mechanism in human's conceptual system manifests itself an effective way for language communication. Although being intuitively sensible for human, metaphor detection is still a challenging task due to the subtle ontological differences between metaphorical and non-metaphorical expressions. This work proposes a modality enriched deep learning model for tackling this unsolved issue. It provides a new perspective for understanding metaphor as a modality shift, as in 'sweet voice'. It also attempts to enhance metaphor detection by combining deep learning with effective linguistic insight. Extending the work at Wan et al. (2020), we concatenate word sensorimotor scores (Lynott et al., 2019) with word vectors as the input of attention-based Bi-LSTM using a benchmark dataset-the VUA corpus. The experimental results show great F1 improvement (above 0.5%) of the proposed model over other methods in record, demonstrating the usefulness of leveraging modality norms for metaphor detection.

Label-Enhanced Hierarchical Contextualized Representation for Sequential Metaphor Identification

“…We compare our model with existing approaches which do not use external knowledge. We do not compare with the works that divided the dataset into the train set and test set by themselves, such as Wan and Xing (2020). Since Gao et al (2018) and Mao et al (2019) used a different subset of VUA, we use the results reported by Neidlein et al (2020) on VUA ALL POS and VUA Verbs for comparison.…”

“…Recent metaphor researches (Gao et al, 2018;Mao et al, 2019), and ACL 2020Metaphor Shared Task(Leong et al, 2020 regard it as a sequence labeling task. Although many previous works have explored ways to enhance the contextualized representation within a sentence (Gao et al, 2018;Mao et al, 2019), or to introduce some external knowledge (Rohanian et al, 2020;Wan and Xing, 2020), most of them do not make full use of the information in the dataset, from which the metaphor identification process may benefit.…”

Contextualized Sensorimotor Norms: multi-dimensional measures of sensorimotor strength for ambiguous English words, in context

“…One approach attempts to predict these judgments about concreteness or salient sensorimotor features from LM representations, with varying degrees of success (Thompson and Lupyan, 2018;Turton et al, 2020;Chersoni et al, 2020;Utsumi, 2020). Another approach uses sensorimotor features to augment the ability of an LM on an applied task, such as the GLUE benchmark (Kennington, 2021) or metaphor detection (Wan et al, 2020b). Ass mentioned in Section 1, these experiments are limited in that the sensorimotor features themselves were obtained for words in isolation.…”

A Survey on Computational Metaphor Processing Techniques: From Identification, Interpretation, Generation to Application

“…The modality norm describes every word in six main senses (auditory, gustatory, haptic, visual, olfactory, and interoceptive). Given the hypothesis that a metaphor showed a shrift in modality from source to target concepts, Wan et al (2020) concatenated the modality norm 22 with word embeddings (GloVe (Pennington et al, 2014)). The results outperformed several BERT-based baselines, showing that the modality norm is a helpful feature in identifying metaphors.…”