A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi- X Perspectives (2022)

Abstract: Irony is a ubiquitous figurative language in daily communication. Previously, many researchers have approached irony from linguistic, cognitive science, and computational aspects. Recently, some progress have been witnessed in automatic irony processing due to the rapid development in deep neural models in natural language processing (NLP). In this paper, we will provide a comprehensive overview of computational irony, insights from linguisic theory and cognitive science, as well as its interactions with downstream NLP tasks and newly proposed multi-X irony processing perspectives.

## introduction irony, which generally refers to the expressions that have opposite literal meanings to real meanings, is a representative rhetoric device in human languages @xcite . for example, in sentences i just love when you test my patience!!, and had no sleep and have got school now, compared to their literal meanings, both sentences are conveying reverse meanings and emotions, which mean not love and not happy. people are using ironies to communicate their affective states more implicitly or explicitly, to strengthen the claims depending on the needs, which contribute to neologism across human languages. however, the inner incongruity makes ironic expressions harder for machines to understand. consequently, accurate irony processing systems are essential and challenging for downstream tasks and natural language understanding (nlu) research. previously, many researchers approached irony processing from various perspectives. the last survey in computational irony was published seven years ago @xcite and mainly focused on irony detection and pragmatic context model. therefore, a systematic and comprehensive survey on automatic irony processing remains ab-sent, encouraging us to focus on a review of advances in irony processing, from traditional machine learning, recurrent neural networks (rnns) methods, to deeper pretrained language models (plms) throughout. besides, del pilar salas-zárate et al. (2020) revealed the great imbalance in figurative language research, in which sarcasm was dominant and twice as much as irony research. with this review, we aim to encourage a balanced and equal research environment in figurative languages. moreover, the design and further improvement in present irony processing systems should be concatenated with both the theoretical accounts in irony theories and its role in communication, followed by downstream applications in nlp tasks. generally, most researchers approached irony processing as a single irony detection branch and three main shared tasks @xcite @xcite all focused on irony detection, whereas other aspects are constantly under-explored. and theoretical-informed or cognitive-informed discussions are rarely seen in irony research compared to tiny amendments in neural networks' architectures. in this paper, we aim to offer a comprehensive review in irony processing from machine learning, linguistic theory, cognitive science, and newly proposed multi-x perspectives and to evaluate irony processing in nlp applications. the remaining sections are organized as follows. in the second part, we will approach irony theories in linguistics and cognitive science research, and discuss the concrete differences between irony and sarcasm. then, we will review irony datasets in world languages and discuss potential problems in annotation schemes throughout. in the fourth part we will retrospect research progress in automatic irony processing, including traditional feature engineering, neural network architectures, to plms and relatively under-explored research fields in irony processing. finally, we will discuss irony's in-teractions with downstream nlp tasks like sentiment analysis and opinion mining, and multi-x perspectives for further development in computational irony research. ## irony processing systems in this section, we will discuss the progress of irony processing systems comprehensively, organized along the development of machine learning and deep learning. ## conclusion in this paper, we reviewed the development in automatic irony processing from underexplored theo-retical and cognitive science to computational perspectives, and offered a comprehensive analysis in future directions. we hope that our work and thinking will encourage further interdisciplinary research between linguistics and human language technology, motivate the research interests in irony and even, figurative languages.