Initial exploration into sarcasm and irony through machine translation

In this paper, we investigate sarcasm and irony as seen through a novel perspective of machine translation. We employ various techniques for translation, comparing both manually and automatically translated datasets of irony and sarcasm. We first clarify the definitions of irony and sarcasm and present an exhaustive field review of studies on irony both from purely linguistic as well as computational linguistic perspectives. We also propose a novel evaluation metric for the purpose of evaluating translations of figurative language, with a focus on machine-translated irony and sarcasm. The constructed English and Chinese parallel dataset includes polarized content from tweets as well as forum posts, categorized by irony types. The preferred translation model, mBART-50, is identified through a thorough experimental process. Optimal translation settings and the best-finetuned model for irony are explored, with the most effective model being finetuned on both ironic and non-ironic data. We also experimented which types of irony are best suitable for training in this specific task — short microblogging messages or longer forum posts. Moreover, we compare the capabilities of a well fine-tuned mBART to a prompt-based method using the recently popular ChatGPT model, with the conclusion that the former still outperforms the latter, although ChatGPT without any training can be considered as a “good enough” ad hoc solution in the case of a lack of data for training. Finally, we verify if the translated data – either manually, or with an MT model – can be used as training data in a task of irony detection. We believe that the presented research can be expanded into languages other than the presented here Chinese and English, which together with the ability to detect various categories of irony, could contribute to deepening the understanding of figurative language, especially irony and sarcasm.