i C ompass at NLP 4 IF -2021–Fighting the COVID -19 Infodemic (2021)

Abstract: This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021. This task is accomplished using a variety of techniques. We used state-of-the-art contextualized text representation models that were fine-tuned for the downstream task in hand. ARBERT, MARBERT,AraBERT, Arabic ALBERT and BERT-base-arabic were used. According to the results, BERT-base-arabic had the highest 0.784 F1 score on the test set.

## introduction in recent years, there has been a massive increase in the number of people using social media (such as facebook and twitter) to share, post information, and voice their thoughts. the increasing number of users has resulted in the development of an enormous number of posts on twitter. although social media networks have enhanced information exchange, they have also created a space for antisocial and illegal activities such as spreading false information, rumors, and abuse. these anti-social behaviors intensify in a massive way during crisis cases, creating a toxic impact on society, either purposely or accidentally. the covid-19 pandemic is one such situation that has impacted people's lives by locking them down to their houses and causing them to turn to social media. since the beginning of the pandemic, false information concerning covid-19 has circulated in a variety of languages, but the spread in arabic is especially harmful due to a lack of quality reporting. for example, the tweet " 40 ## dataset description the provided training dataset of the competition, fighting the covid-19 infodemic arabic, consists of 2536 tweets and the development dataset con-sists of 520 tweets @xcite . the data was labelled as yes/no questions answering seven questions: questions 2,3,4 and 5 will be labelled as nan if the answer to the first question is no. the tweets are in modern standard arabic (msa) and no other arabic dialect was observed. data was preprocessed by removing emojis, urls, punctuation, duplicated characters in a word, diacritics, and any non arabic words. we present an example sentence before and after preprocessing: ## 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. recent similar work was conducted for arabic which is increasingly gaining attention. in our work, we used three bert arabic variants: arabert (antoun et al., 2020), arbert (abdul-mageed et al., 2020), marbert (abdul-mageed et al., 2020) and arabic bert (safaya et al., 2020). added-on, we used the xlarge version arabic albert foot_1 . 3.1 arabert arabert (antoun et al., 2020), was trained on 70 million sentences, equivalent to 24 gb of text, covering news in arabic from different media sources. it achieved state-of-the-art performances on three arabic tasks including sentiment analysis. yet, the pre-training dataset was mostly in msa and therefore can't handle dialectal arabic as much as official arabic. ## development dataset results we have validated our models through the development dataset as mentioned in the data section. the results of all models were close but the bert-basearabic achieved the best results performing 78.27% f1 score. for reference, and to compare with other models, we also showcase the results obtained with arbert, arabert, and arabic albert in ## official submission results the result of all the models used are very close. however, bert-base-arabic outperformed all other models. this may be due to the pretrained data for bert-base-arabic. the final version has some non-arabic words inlines. also, the corpus of bertbase-arabic and vocabulary set are not restricted to msa, they contain some dialectical arabic too which can boost the model performance in terms of data from social media. ## conclusion this paper describes the system built in the nlp4if 2021 shared task , along with comprehensive results.