IISERB Brains at S em E val-2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in E nglish (2022)

Abstract: This paper describes the system architectures and the models submitted by our team “IISERB Brains” to SemEval 2022 Task 6 competition. We contested for all three sub-tasks floated for the English dataset. On the leader-board, we got 19th rank out of 43 teams for sub-task A, 8th rank out of 22 teams for sub-task B, and 13th rank out of 16 teams for sub-task C. Apart from the submitted results and models, we also report the other models and results that we obtained through our experiments after organizers published the gold labels of their evaluation data. All of our code and links to additional resources are present in GitHub for reproducibility.

## introduction sarcasm in spoken or written form is a type of verbal irony that indicates the difference between the literal and intended meanings of an utterance @xcite . while many people use it as a bitter remark to mock or ridicule a target @xcite , some also use it as a joke to amuse others @xcite . sarcasm often used together or interchangeably with other ironic categories, is considered an essential component of human communication. a large portion of the web and social media text is sarcastic, which creates a challenge for traditional natural language processing (nlp) tasks like sentiment classification, opinion mining, harassment detection, author profiling etc. since these systems are deployed widely across various industries, administration, data analysts etc, designing a robust sarcasm detecting component would help the downstream tasks substantially. the semeval-2022 task 6 @xcite identifies some of the challenges persisting till now, particularly in english and arabic texts. our team participated in the tasks floated for the english language under the name "iiserb brains". the organizers have floated three subtasks: (i)sub-task a to detect whether a given text is sarcastic, (ii)sub-task b to identify which ironic category the sarcastic tweet belongs to, and (iii) sub-task c to given two pieces of text, identify the sarcastic one. in this paper, we report the details of our participating systems and their performance on the evaluation data. the paper also contains details of additional experiments that were a part of our participation in the shared task. the paper is organised as follows. section 2 describes past work related to the sub-tasks, while section 3 presents the dataset and task details. following that, section 4 reports the details of our systems followed by experiments and results in section 5. finally, in section 6, we conclude the paper with a summary of the overall results. with our submitted systems, we obtained the 19th rank out of 43 teams for sub-task a, 8th rank out of 22 teams for sub-task b, and 13th rank out of 16 teams for sub-task c. our code is made public to ensure the reproducibility of our results. ## related work sarcasm has been an interesting research topic for computational linguists for a long time @xcite . researchers have studied the topic from depth(sarcasm sub-categories, linguistic nuances etc.) and breadth (multi-modal sarcasm @xcite , multi-lingual sarcasm @xcite , sarcasm targets @xcite , etc). a considerable amount of work has been reported on the task 'sarcasm detection' itself. this task particularly deals with identifying whether a given text is sarcastic. the sub-task a proposed by the organizers also belongs to this task. while the traditional approaches have used feature-based machine learning models, recent approaches mostly relied on deep-neural models to report state of art performance. lexical features like emoticons, special characters, word-patterns have always been preferred features along with semantic features like parts-of-speech tags, contrasting sentiments etc @xcite (veale and hao, 2010) @xcite @xcite in traditional approaches. deep-neural methods, on the other hand, learn latent features for the same task. they rely on architectures like rnns, cnns, transformers etc. in their models @xcite @xcite @xcite . pre-trained language models like bert, roberta, xlnet etc, have been extensively used as token-encoders in these models. identifying sarcastic intention has always been a challenging task, even humans sometimes have difficulties. recently, researchers have started focusing on contextual information such as author context @xcite , multi-modal context @xcite , eye-tracking information @xcite , or conversation context @xcite to capture it. the sub-task a is related to shared tasks in the domain of figurative analysis such as a semeval task on irony detection in twitter that focuses on the utterances in isolation. ## conclusion in this paper, we discussed our models for different sub-tasks proposed by semeval 2022 task 6 organizers for the english dataset. we experimented