Modality Enriched Neural Network for Metaphor Detection

Proceedings of the 28th International Conference on Computational Linguistics, pages 3036–3042 Barcelona, Spain (Online), December 8-13, 2020 3036 Modality Enriched Neural Network for Metaphor Detection Mingyu Wan1,2 and Baixi Xing3 1School of Foreign Languages, Peking University 2Department of Chinese Bilingual Studies, The Hong Kong Polytechnic University 3School of Information Science Language, Beijing Language and Culture University wanmy@pku.edu.cn, xingbaixi@gmail.com 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. 1 Introduction Metaphor is a common linguistic mechanism that encodes versatile ontological information accompanied by phenomena of e.g. domain transfer (Ahrens and Jiang, 2020), concreteness change (Maudslay et al., 2020) or semantic surprise (Zhang and Barnden, 2013) etc. In general, metaphor usually involves certain concept transfer from one domain (Source) to another (Target), as demonstrated in the example ‘sweet smile’ (using the taste modality to describe the vision modality). It is a fundamental way for people to relate our physical and familiar social experiences to a multitude of other subjects and contexts that are more complex, implicit or less known (Lakoff and Johnson, 1980; Lakoff and Johnson, 2008). Detecting metaphors in texts is essential for capturing the authentic meaning of a language unit (e.g. word, phrases or even sentences) when the language is beyond literal interpretations, which can benefit a lot of language processing applications, such as machine translation, dialogue systems and sentiment analysis (Tsvetkov et al., 2014). Although detecting metaphors is subconsciously effortless for hu- man, it is actually a great challenge to existing technologies due to the subtle semantic and ontological differences between metaphorical and non-metaphorical ones (Leong et al., 2018; Leong et al., 2020). This work proposes a modality-enriched deep learning model for metaphor detection based on the idea that the basic senses (touch, hearing, smell, taste, vision and interception) and action affectors (mouth/throat, hand/arm, foot/leg, head) indicated by the sensorimotor norms (Lynott et al., 2019) of words provide crucial information for metaphoricity inference. As a next step of Wan et al. (2020) which only adopts simple statistical models, we build a more complex model combining neural networks with modality information. For standard reference, we adopt the benchmark dataset of the first and second shared tasks of metaphor detection–the VUA corpus (Klebanov …

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