High Tech team at S em E val-2022 Task 6: Intended Sarcasm Detection for A rabic texts (2022)

Abstract: This paper presents our proposed methods for the iSarcasmEval shared task. The shared task consists of three different subtasks. We participate in both subtask A and subtask C. The purpose of subtask A was to predict if a text is sarcastic while the aim of subtask C is to determine which text is sarcastic given a sarcastic text and its non-sarcastic rephrase. Both of the developed solutions used BERT pre-trained models. The proposed models are optimized on simple objectives and are easy to grasp. However, despite their simplicity, our methods ranked 4 and 2 in iSarcasmEval subtask A and subtask C for Arabic texts.

## introduction nowadays, social media users provide a huge amount of text, images and videos. this large amount of data contains useful information (users ideas, opinions, events, etc) for various domains such as stock predictions, marketing, or politics. in order to benefit from these data, new fields of study have been introduced including sentiment analysis, opinion mining, author profiling, and harassment detection @xcite @xcite . natural language processing (nlp) algorithms are used extensively in these fields to extract useful information. for instance, to determine whether a given product has a positive or negative sentiment in the market, we can apply nlp techniques to analyse a list of twitter posts to infer a sentiment about the product. according to oxford dictionary, sarcasm is "the use of irony to mock or convey centempt". sarcastic text convey negative implied sentiment, however it can have positive, negative, or no surface sentiment. sarcasm is commonly used in social media, thus it introduces errors in various tasks such as sentiment analysis and opinion mining. this is explained in the work of @xcite , it shows a significant drop in sentiment polarity classification performance when processing sarcastic tweets, compared to non-sarcastic ones. in this context, the task isarcasmeval: intended sarcasm detection in english and arabic @xcite is organized by semeval 2022. the main tasks consists of three subtask: in this paper, we describe our contribution to is-arcasmeval shared task, arabic language only. for subtask a, we built a bert-based neural network @xcite classifier to determine whether a tweet is sarcastic or not. our model obtained the fourth best performance in the subtask a. for subtask c, we built also a bert-based classifier to detect the sarcastic text from two text that convey the same meaning. we scored the second best performance in the subtask c. the results are promising and there is much room for improvement. the rest of the paper is organized as follows: section 2 presents our method overview; section 3 provides performance evaluation; section 4 concludes the paper and provides future work. ## method overview in this section, we first describe how we split data to evaluate our models. next, we explain preprocessing steps. next, we discuss our models for each subtask, and the experimental setup we used. we also provide illustrations and examples, when necessary. ## performance evaluation in this section, we present the performance of various models trained on both subtasks a and c. ## conclusion we developed two methods for sarcasm prediction. the first one aim to predict if a text is sarcastic or not. this method is based on arabertv02twitter pretrained model which extract valuable features from raw text. we achieved the fourth top performance in the isarcasmeval subtask a for arabic with a 46.84% f1 score for the sarcastic class. the second model has the objective to detect the sarcastic text given two texts that convey the same meaning. we trained a bert-based model that take as input two text and predict the sarcastic one. we ranked 2 in the leaderboard with 88.5% accuracy. in future work, we plan to improve the performance of our models by using linguistic rules and some external datasets.