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Metaphor is not only a linguistic phenomenon but also reflects the concept projection between source and target domains in human cognition. Previous sequence tagging-based metaphor identification methods could not model the concept projection, resulting in a limitation that the outputs of these models are unexplainable in the predictions of the metaphoricity labels. In this work, we propose the first explainable metaphor identification model, inspired by Conceptual Metaphor Theory. The model is based on statistic learning, a lexical resource, and a novel reward mechanism. Our model can identify the metaphoricity on the word-pair level, and explain the predicted metaphoricity labels via learned concept mappings. The use of the reward mechanism allows the model to learn the optimal concept mappings without knowing their true labels. Our method is also applicable for the concepts that are out of training domains by using the lexical resource. The automatically generated concept mappings demonstrate the implicit human thoughts in metaphoric expressions. Our experiments show the effectiveness of the proposed model in metaphor identification, and concept mapping tasks, respectively.

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Metaphoric expressions are a special linguistic phenomenon, frequently appearing in everyday language. Metaphors do not take their literal meanings in contexts, which may cause obstacles for language learners to understand them. Metaphoric expressions also reflect the cognition of humans via concept mappings, attracting great attention from cognitive science and psychology communities. Thus, we aim to develop a computational metaphor processing online system, termed MetaPro Online 1 , that allows users without a coding background, e.g., language learners and linguists, to easily query metaphoricity labels, metaphor paraphrases, and concept mappings for non-domainspecific text. The outputs of MetaPro can be directly used by language learners and natural language processing downstream tasks because MetaPro is an end-to-end system.

Metaphor is a figurative language that has been frequently used in our daily lives. Due to its significance for downstream natural language processing tasks, such as machine translation and sentiment analysis, computational metaphor processing has set off an upsurge in the community. With the development of Artificial Intelligence, an increasing number of technological tools and frameworks have been proposed in this domain. In this article, we aim to comprehensively summarize and categorize previous computational metaphor processing approaches regarding metaphor identification, interpretation, generation, and application. Meanwhile, we compare the strengths and weaknesses of current works and conceive future directions in this field.