X - P u D u at S em E val-2022 Task 6: Multilingual Learning for E nglish and A rabic Sarcasm Detection (2022)

Abstract: Detecting sarcasm and verbal irony from people’s subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios. This paper describes the X-PuDu system that participated in SemEval-2022 Task 6, iSarcasmEval - Intended Sarcasm Detection in English and Arabic, which aims at detecting intended sarcasm in various settings of natural language understanding. Our solution finetunes pre-trained language models, such as ERNIE-M and DeBERTa, under the multilingual settings to recognize the irony from Arabic and English texts. Our system ranked second out of 43, and ninth out of 32 in Task A: one-sentence detection in English and Arabic; fifth out of 22 in Task B: binary multi-label classification in English; first out of 16, and fifth out of 13 in Task C: sentence-pair detection in English and Arabic.

## introduction sarcasm is the use of language that typically signifies the opposite to mock or convey contempt. as a narrow research field in natural language processing (nlp), sarcasm detection is a particular case in the spectrum of sentiment analysis, with important implications for a slew of nlp tasks, such as sentiment analysis, opinion mining, author profiling, and harassment detection. in the textual data, these tonal and gestural clues like heaving tonal stress and rolling of the eyes are missing, making it more difficult for machines. the sarcastic intention of human annotators has potentially hindered the training and evaluation process in detecting the genuine emotions and positions of the natural language. thus, this task @xcite adopted a novel data collection method (oprea and magdy, 2020), where authors themselves label the training samples. for sarcastic texts, the authors also rephrase them into non-sarcastic ones. then, linguistic experts further checked the scathing pieces and la-beled them into sub-categories of sarcasm defined by @xcite : sarcasm, irony, satire, understatement, overstatement, and rhetorical question. this semeval task requires the identification of sarcasm in either one sentence or sentence pairs in various language settings, which consists of three subtasks: our method employed various multilingual or mono-lingual pre-trained language models, such as ernie-m (ouyang et al., 2020) and deberta @xcite to address each component of this task, with a bunch of fine-tuning and ensemble techniques. our system finally achieved • 2nd out of 43 and 9th out of 32 in english and arabic subtasks in task a; ## previous work after detecting sarcasm in the speech was firstly proposed in @xcite , sarcasm detection has attracted extensive attention in the nlp community. afterward, sarcasm detection in the transformer 0.05 0.95 0.07 0.73 0.03 0.12 0.85 0.02 0.98 0.02 sarcastic non-sarcastic sarcasm irony satire understatement overstatement rhetorical_question sent1 sent2 task a english: i want to see drew locy cry. arabic: ‫ﻋﯿﺎش‬ ‫راﻣﻲ‬ ‫ﻣﺎﺑﺌﻼ‬ ‫ﺑﺤﺒﻚ‬ ‫وﻏﯿﺮ‬ ‫وﺑﺪﺋﻼ‬ ‫ﺑﻐﻨﯿﻼ‬ task b english: 6 more hours and then a whoppingly massive 2 days off work! task c english: trying to know all this history tonight is gonna kill me. [sep] trying to know all this history is going to be a challenge. arabic: ‫ﯾﺼﺤﺒﻲ‬ ‫دي‬ ‫اﻟﻤﮭﻠﺒﯿﺔ‬ ‫اﯾﺔ‬ [sep] ‫اﻟﺠﻤﺎل‬ ‫ھﺬا‬ ‫ﻣﺎ‬ classifier softmax softmax sigmoid figure 1: fine-tuning pre-trained models on the isarcasmeval data. recently, pre-trained language models such as bert @xcite , ernie @xcite , and gpt-3 @xcite , have set the new state-of-the-art in a wide range of nlp benchmarks, such as glue @xcite . @xcite evaluated the performance of pretrained model using feature-based and fine-tuning methods on irony detection in english tweets, finding the latter is better. meanwhile, there is also a surge of applying pre-trained models in sarcasm detection @xcite @xcite . our system explored the multilingual and monolingual pre-trained language models to testify their fine-tuning performance on english and arabic sarcasm detection tasks. ## conclusion we present our system that participated in se-meval task 6 and employ the multilingual learning method to train the english and arabic tasks jointly. we empirically find that it confers benefits in specific scenarios and outranks the monolingual pretrained models on arabic tasks. however, we do not adopt other arabic-specific pre-trained models, which is also worth comparing. in the future, it is a promising direction to explore different sarcasm detection approaches under multilingual settings.