Recent advances in neural metaphor processing: A linguistic, cognitive and social perspective (2021)

Abstract: Metaphor is an indispensable part of human cognition and everyday communication. Much research has been conducted elucidating metaphor processing in the mind/brain and the role it plays in communication. in recent years, metaphor processing systems have benefited greatly from these studies, as well as the rapid advances in deep learning for natural language processing (NLP). This paper provides a comprehensive review and discussion of recent developments in automated metaphor processing, in light of the findings about metaphor in the mind, language, and communication, and from the perspective of downstream NLP tasks.

## introduction metaphor is a figurative device that allows us to portray one domain, often abstract, in terms of another that is typically more concrete @xcite . in the sentences his eyes lightened at the news, or her mood darkened, the abstract domain of emotion is described in terms of the more concrete domain of brightness. through cross-domain associations and comparisons, metaphor enables us to communicate complex abstract concepts, to convey our affective states, to reinforce our arguments and, ultimately, to forge entirely novel word meanings. metaphor is also ubiquitous in language, occuring on average in every three sentences, according to corpus-linguistic research @xcite . this makes accurate interpretation of metaphorical language essential for many nlp tasks and applications. a systematic and comprehensive survey of metaphor processing systems was published five years ago @xcite . since then, automated metaphor processing has shifted focus towards neural approaches and there has been much activity in the area, including two shared tasks @xcite . the development has kept pace with the development of deep learning techniques, and the state-of-the-art performance has been advanced very quickly, stressing the need for a new survey focusing on this recent progress. moreover, the design and evaluation of metaphor processing systems should be informed by both the cognition of metaphor and its role in communication, as well as the potential applications of these systems. it is common practice in the nlp community to divide metaphor processing into two sub-tasks, metaphor identification and interpretation, and the two shared tasks encourage the use of a common task definition and particular metaphor corpora for the development of metaphor identification systems. however, there has been little discussion about whether or how well the existing systems address theoretical and cognitive considerations in the use of metaphor and how insights and models produced by this work could inform and benefit the wider natural language understanding (nlu) research. this paper aims to provide a starting point for such a discussion, by reviewing recent advances in metaphor processing in the context of contemporary theories of metaphor, its sociolinguistic aspects and the needs of nlp applications. we begin with an overview of theories and findings about metaphor in the mind/brain, language, and communication. we then review metaphor processing approaches published in the last 5 years, as well as the datasets they used. we provide an analysis of these models from the linguistic and cognitive perspective and propose some future directions for modelling social aspects of metaphor use (e.g. the intent behind metaphor use, extended metaphor and metaphoric framing), as well as the underaddressed tasks of metaphor interpretation and generation. finally, we discuss how elements of metaphor processing can be integrated in the real-world applications, such as machine translation, opinion mining, dialogue modelling, and modelling argumentative discourse (e.g., political discourse). ## metaphor datasets metaphoricity annotations @xcite released a dataset (henceforth: tsv) consisting of an equal number (884) of metaphorical and non-metaphorical adjective-noun (an) phrases collected from the web. the phrases were stated to be verified by multiple annotators, but the criteria for metaphor annotation were not provided. the dataset released by mohammad et al. (2016) (henceforth: moh) consists of 1639 (1230 literal and 409 metaphorical) sentences extracted from wordnet, manifesting the use of 440 verbs. metaphoricity annotation of the verb uses was obtained through crowdsourcing. note that the word-net sentences are mainly instances of conventional metaphor. the specified association between word senses and metaphoricity makes it easier to determine the source and target domains involved. the language computer corporation metaphor datasets (lcc) @xcite contain linguistic metaphors extracted from web corpora for a small set of target domains in four languages: english, spanish, russian, and farsi. metaphoricity is annotated at sentence level, and the conceptual metaphors and affect information is specified. vu amsterdam metaphor corpus (vua) @xcite is the fruit of the authors' application of mipvu. the metaphoricity of each lexical unit (187,570 in total) in a subset of the british national corpus (bnc consortium, 2007) was annotated. vua is the only metaphor corpus used in studies of automated metaphor identification that is built by cognitive linguists, and the only one that deals with the metaphoricity of function words. mohammad et al. (2016) also obtained literal paraphrases of the sentences, in which the metaphorically used verbs are replaced by their synonyms (171 pairs of sentences in total), selected by the authors. note that the literal paraphrases were considered to convey less emotion than the original metaphorical sentences in their experiment. it is therefore questionable to what extent the paraphrases capture the connotations of the metaphorical sentences. ## discussion metaphor identification word-level metaphor identification is the most studied topic in automated metaphor processing. the systems utilise the latest nlp techniques and findings about metaphor in cognitive sciences; the state-of-the-art performance has been advanced quickly. a limitation in these studies, however, is that the systems do not deal with different types of metaphors. the high percentage of conventional metaphors in vua @xcite implies that systems trained on the corpus may not capture conventional and novel metaphor uses equally well. it is the processing of novel metaphors that is a particular challenge for nlu systems, and the current experimental setup does not allow us to explicitly benchmark metaphor identification systems in this ability. the same goes for deliberate metaphor use, which is essential for modelling metaphor in communication and applies to both conventional and novel metaphors. whilst there have been huge advances in perfor-mance in word-level metaphor identification, the broader tasks of identifying conceptual metaphors, extended metaphors, and metaphoric framing, have been largely ignored. conceptual metaphors form the framework for both extended metaphors and metaphoric framing, from a cognitive perspective, and these latter two are a key part of the intentional use of metaphor in communication. ## conclusion in this paper, we reviewed recent advances in automated metaphor processing and brought to light its less explored aspects, pertaining to cognitive processing of metaphor, its role in communication and its connection with downstream nlp applications. we hope that our work will encourage further in-terdisciplinary research on metaphor and facilitate progress in this important and fascinating area.