i C ompass at Shared Task on Sarcasm and Sentiment Detection in A rabic (2021)

Abstract: We describe our submitted system to the 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic (Abu Farha et al., 2021). We tackled both subtasks, namely Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). We used state-of-the-art pretrained contextualized text representation models and fine-tuned them according to the downstream task in hand. As a first approach, we used Google’s multilingual BERT and then other Arabic variants: AraBERT, ARBERT and MARBERT. The results found show that MARBERT outperforms all of the previously mentioned models overall, either on Subtask 1 or Subtask 2.

## introduction sentiment analysis and especially sarcasm detection in arabic are challenging tasks, not only because of the labelled data scarcity, but also because of the complexity of sarcasm detection for models. sarcasm is highly dependent on the culture, gender and other aspects like dialects. indeed, the arabic language has many variants and dialects, other than the modern standard arabic (msa). even if some dialects share some vocabulary, they still differ according to countries, where each dialect has its own specifications which makes an impact on the task. for example, the sentence " ## data the provided training dataset of the competition, arsarcasm-v2 @xcite , consists of 12548 tweets, labelled with the tweet's sentiment class (pos: positive, neg: negative and neu: neutral), its sarcasm class (true: sarcastic, false: non-sarcastic), and its dialect (namely "msa", "gulf", "egypt", "levant" and "magreb"). the initial released training data was provided without the validation dataset. for this reason, we had to split the data to be able to validate our models. we chose a 80:20 split given the size of the dataset. we chose to have a balanced validation set. the distributions for the two subtasks are as shown in ## system description pretrained contextualized text representation models have shown to perform effectively in order to make a natural language understandable by machines. bidirectional encoder representations from transformers (bert) @xcite is, nowadays, the state-of-the-art model for language understanding, outperforming previous models and opening new perspectives in the natural language processing (nlp) field. as a first approach, we used multilingual cased bert model (hereafter mbert) @xcite . recent similar work have been conducted for arabic which is increasingly gaining attention. as a second approach, we used three bert arabic variants: arabert (antoun et al., 2020), arbert (abdul-mageed et al., 2021) and marbert (abdul-mageed et al., 2021). ## results we have validated our models through balanced validation sets as mentioned in the data section, after splitting the training dataset into training and validation sets with a 80:20 ratio. the models based on marbert achieved the best results. we believe this is because marbert was trained mostly on dialectal arabic which was underrepresented in previous pretrained models. since this task's data is multi-dialectal, this model is expected to achieve the best performances. ## discussion ## conclusion four language models were used to classify sentiment and to detect sarcasm (mbert, arabert, arbert and marbert). the best results were obtained by marbert for both tasks with different hyperparameters, which was selected for the