The Elephant in the Room: Ten Challenges of Computational Detection of Rhetorical Figures (2024)

Abstract: Computational detection of rhetorical figures focuses mostly on figures such as metaphor, irony, or analogy. However, there exist many more figures that are neither less important nor less prevalent. We wanted to pinpoint the reasons why researchers often avoid other figures and to shed light on the challenges they struggle with when investigating those figures. In this comprehensive survey, we analyzed over 40 papers dealing with the computational detection of rhetorical figures other than metaphor, simile, sarcasm, and irony. We encountered recurrent challenges from which we compiled a ten point list. Furthermore, we suggest solutions for each challenge to encourage researchers to investigate a greater variety of rhetorical figures.

## introduction rhetorical figures such as metaphor, alliteration, or irony are present in our daily lives. they make language vivid, more emotional, or more persuasive. each figure has a special function, e.g., figures with repetition create more emphasis @xcite , while sarcasm and irony are often used in the context of hate speech @xcite . to understand the often non-literal meaning and subtle nuances of a text containing rhetorical figures, it is important to reliably detect those figures computationally. furthermore, the performance of classical nlp tasks improves when taking features of rhetorical figures into account. this was demonstrated for sentiment analysis @xcite , argumentation mining @xcite ), text summarization @xcite , and hate speech and abusive language detection @xcite . most detection approaches only consider the rhetorical figures metaphor @xcite @xcite @xcite @xcite , or irony and sarcasm @xcite @xcite . however, the silva rhetoricae foot_0 @xcite , an online resource for rhetorical figures and their descriptions, lists 435 different rhetorical figures. most of those figures are neither less present nor less important than metaphor. for example, the figure antithesis is important in environmental @xcite or populist communication @xcite , litotes is important in sentiment analysis @xcite , and polyptoton can highlight similarities while showing a distinction @xcite . we believe that it is essential to pay attention to the other figures, too. in this survey, we investigate the main challenges and problems researchers struggle with when computationally dealing with those figures. we examined over 40 papers describing computational detection approaches for rhetorical figures other than metaphor, simile, analogy, sarcasm, and irony. the figures range from a like alliteration to z like zeugma. we focus on papers that consider the detection of rhetorical figures in written text, as speech or multimodal approaches further increase both the complexity and challenges. we were looking on google scholar 2 for relevant work by searching for figure names along with "detection", and including relevant related work. we explicitly did not look only into libraries such as the acl anthology 3 , as the field of rhetorical figure detection is not that represented at big conferences. from these results, we compiled a comprehensive list of ten key chal-lenges and problems that show a recurrent pattern. we also provide suggestions for overcoming those challenges in order to further strengthen the field of computational detection of rhetorical figures in the future. ## rhetorical figure detection: ten challenges we present ten challenges that most researchers face when trying to computationally identify rhetorical figures. we also suggest solutions for each of the challenges. ## conclusion our comprehensive review of over 40 papers highlights the prevalent challenges in computationally detecting rhetorical figures. as each rhetorical figure plays a crucial role in our daily communication, we urge researchers to tackle the presented challenges. when we can understand the non-literal and subtle meaning of rhetorical figures, we can improve existing systems and better understand language. in the future, we would like to see some of the suggestions implemented. furthermore, we aim to inspire researchers to also focus on the detection of lesser-known figures.