Modeling Conceptual Attribute Likeness and Domain Inconsistency for Metaphor Detection (2023)

Abstract: Metaphor detection is an important and challenging task in natural language processing, which aims to distinguish between metaphorical and literal expressions in text. Previous studies mainly leverage the incongruity of source and target domains and contextual clues for detection, neglecting similar attributes shared between source and target concepts in metaphorical expressions. Based on conceptual metaphor theory, these similar attributes are essential to infer implicit meanings conveyed by the metaphor. Under the guidance of conceptual metaphor theory, in this paper, we model the likeness of attribute for the first time and propose a novel Attribute Likeness and Domain Inconsistency Learning framework (AIDIL) for word-pair metaphor detection. Specifically, we propose an attribute siamese network to mine similar attributes between source and target concepts. We then devise a domain contrastive learning strategy to learn the semantic inconsistency of concepts in source and target domains. Extensive experiments on four datasets verify that our method significantly outperforms the previous state-of-the-art methods, and demonstrate the generalization ability of our method.

## introduction metaphor is pervasive in various forms of language expressions, such as political speech, advertising text, and literary work. merriam-webster dictionary defines metaphor as "a figure of speech in which a word or phrase literally denoting one kind of object or idea is used in place of another to suggest a likeness or analogy between them" 1 . metaphor detection aims to distinguish between metaphorical and literal expressions in text. it has gained increasing research interest in recent years and plays a vital role in various nlp applications that need to understand implicit semantics, such as target concept similar attributes liquid, consuming constantly gasoline figure 1 : illustration of metaphor understanding with similar attributes. the target concept gasoline and the source concept (e.g., soda and water) are domaininconsistent with their intermediate similar attributes (e.g., liquid and consuming constantly). these similar attributes help understand the implicit meaning conveyed by the metaphor, that is, drinking gasoline implies consuming gasoline a lot like consuming liquid. machine translation @xcite , sentiment analysis @xcite , and dialogue system @xcite . existing research on metaphor detection mainly focuses on detecting metaphors at word-pair level @xcite @xcite and token-level @xcite @xcite . early studies construct linguistic features using manually-created resources for metaphor detection, such as abstractness @xcite , imageability @xcite and visibility @xcite . recent studies utilize metaphor identification theories @xcite @xcite , including selection preference violation (spv) @xcite and metaphor identification procedure (mip) @xcite , to help design deep learning methods for metaphor detection. although these methods have achieved promising performances, their underlying theories only utilize contextual clues relevant to the basic meaning of words and their contexts for metaphor identification, lacking the deeper level understanding of conceptual meanings and semantic characteristics associated with metaphor. in contrast to the above metaphor identification theories, a more explainable theory, conceptual metaphor theory (cmt) @xcite , argues that metaphor facilitates a mapping of a set of similar attributes between the concepts in two different domains (i.e. source and target domains). inspired by cmt, @xcite generate the source and target concepts from different domains to benefit metaphor detection. however, their work only considers domaininconsistent information, ignoring the mapping of similar attributes shared by source and target concepts, which provides essential clues to infer the implicit meanings that metaphors tend to convey. according to cmt, metaphor understanding implies a plausible mapping of a set of similar attributes between the concepts in source and target domains. hence, attribute in cmt is a broad characteristic obtained by activating human imagination and association based on commonsense knowledge as well as perceptual experience. fig. 1 gives an example to illustrate metaphor understanding with similar attributes. in this example, attribute likeness (liquid and consuming constantly) is the intermediate association to the implicit meaning (consuming a lot) conveyed by this metaphor. therefore, attribute likeness functions as important associated information for metaphor detection, and the mining of this attribute similarity strongly depends on an appropriate refinement process from plentiful candidate attributes. based on the above considerations, in this paper, we propose an attribute likeness and domain inconsistency learning framework, namely aidil, for word-pair metaphor detection. we devise an attribute siamese network with layer-wise attribute refinement graphs to model attribute likeness information and incrementally filter irrelevant information from plentiful candidate attributes. meanwhile, to exploit information of inconsistent domains, we design a domain contrastive learning strategy on the hidden representations of concepts from source and target domains so as to learn an embedding space with information of domain inconsistency. the two components jointly learn the association and disparity between source and target concepts for effective metaphor detection. additionally, our method is also capable of providing explainable clues like source concepts or learned attribute information in metaphor prediction. the main contributions of our work are as follows: ## related work metaphor detection can be roughly divided into two categories: word-pair level and token level. the former determines whether a word pair is metaphorical or literal, and the latter aims to find metaphorical words in sentences. as word pair is a basic type and the most commonly used linguistic form to express metaphor @xcite , its metaphor detection is the focus of our work. ## problem definition in a word pair, we define the verb in a verb-noun pair or the adjective in an adjective-noun pair as the core word. according to cmt, the other noun is the target concept in a metaphorical word pair or the source concept in a literal word pair. formally, d @xmath0, l k )} ntr @xmath1, where p k is the k-th word pair and l k is the label (metaphorical or literal). every word pair contains a core word w cor and a concept word w con . d @xmath2, l k )} nte @xmath3 training set positive and negative samples classifier conceptnet [cls] drink gasoline [cls] drink gasoline [cls] drink soda [cls] drink water [cls] drink gasoline guide soda gasoline water push push pull … … drink encoder encoder + + attribute siamese network domain contrastive learning contextual representation contextual representation gasoline water classification metaphorical/literal shared (core word) n te instances. the goal of word-pair metaphor detection is to predict the label of each word pair in d te by training a model on d tr . ## method we propose an attribute likeness and domain inconsistency learning framework, namely aidil, for word-pair metaphor detection. fig. 2 shows its overall architecture, which contains three primary components: (1) attribute siamese network, which obtains the attribute vector via refining similar attributes and filtering irrelevant attributes gradually based on two attribute graph sub-networks of source concept and target concept respectively guided by the core word; (2) domain contrastive learning, which performs contrastive learning on representations of concepts from source and target domains to learn the information of domain inconsistency for better generalization of contextual representations; and (3) classification, which utilizes contextual representations and the attribute vector to predict the label (metaphorical/literal). ## conclusion in this paper, we propose an attribute likeness and domain inconsistency learning framework for wordpair metaphor detection. inspired by the conceptual metaphor theory, our framework models attribute likeness with an attribute siamese network via layer-wise attribute refinement graphs and learns domain inconsistency with a domain contrastive learning strategy. experimental results show that our method significantly outperforms the previous word-pair and token-level sota methods, verifying the effectiveness of our proposed framework for metaphor detection.