i C ompass at S em E val-2020 Task 12: From a Syntax-ignorant N-gram Embeddings Model to a Deep Bidirectional Language Model (2020)

Abstract: We describe our submitted system to the SemEval 2020. We tackled Task 12 entitled “Multilingual Offensive Language Identification in Social Media”, specifically subtask 4A-Arabic. We propose three Arabic offensive language identification models: Tw-StAR, BERT and BERT+BiLSTM. Two Arabic abusive/hate datasets were added to the training dataset: L-HSAB and T-HSAB. The final submission was chosen based on the best performances which was achieved by the BERT+BiLSTM model.

## introduction with the freedom of expression privilege granted after the arab countries revolution, sensitive topics such as religion and politics have become popular and widely discussed across social media platforms. however, on the down side, offensive language spreads easily. indeed, recent events; like persian's gulf crisis, the parliamentary-presidential elections held in tunisia or a football game between two arabic clubs; caused intensive debates, most of them took place on social media networks leading to a high emergence of offensive speech. this evokes the need for tools to identify such online offensive language content. analyzing arabic offensive language is significantly challenging due to the complex nature and morphology of the arabic language. furthermore, arabic language in social media is mostly informal and written in arabic dialects. we describe our participation in semeval 2020 task 12 entitled "multilingual offensive language identification in social media" zampieri et al. (2020), specifically task 12 4a-arabic: "arabic offensive language identification in social media" @xcite under the team name "icompass". the task requires to distinguish between offensive (containing any form of non-acceptable language (profanity)) and non-offensive posts. the remainder of the paper is organized as follows: in section 2, we introduce l-hsab and t-hsab datasets. in section 3, we describe the preprocessing step. section 4 introduces the learning strategies and datasets used in the presented models. results are reviewed and discussed in section 5 while section 6 concludes the study. ## arabic offensive language datasets the proposed dataset for the semeval task 12 subtask a-arabic is composed mainly by egyptian dialect comments and few other dialects like libyan, sudanese, syrian, etc. some comments, after the preprocessing phase, in different dialects are given in ## data preprocessing data was preprocessed by cleaning the tweets from the social media-inherited symbols such as (rt,<@xmath0, urls, usernames, dates, retweets, symbols, punctuations, emojis and non-arabic characters, in order to remove noise and to get the arabic text only. before rt @user: . ... \ \ <@xmath1::. . . ## used models and learning strategies in this section, we describe the learning strategies and the different architectures used. the mechanism of each strategy is briefly reviewed. to accomplish this mission, we have used three classification approaches. in addition, we have used an offensive lexicon extracted from l-hsab and t-hsab datasets. the offensive lexicon is composed by offensive words in arabic dialects such as that mean thief, traitor... we have combined the predictions of our classifier with the results of the lexicon-based method to have a hybrid solution. this mixed solution have decreased the results since it can neither identify negations nor detect non-offensive comments that contain offensive words, example in tw-star+lexicon approach has classified this comment as offensive because it contains and , which means thief and traitor, that appear in the lexicon of offensive words and confuse the prediction results of this type of sentences. this approach decreased the performance results as given in ## results and discussion three datasets are provided by semeval 2020 task 12 subtask 4a-arabic: train (7000 comments) for training models, dev (1000 comments) for tuning models, and test (2000 comments) for the official evaluation. data was preprocessed using regular expressions recognition and regular expressions substitution provided by the re python module foot_0 . having the data preprocessed and the features extracted, training dataset was splitted into 80% for training and 10% for cross-validation. we have trained our tw-star, bert, and bilstm models on the dev set. ## conclusion and future work three classification architectures were used to identify offensive language arabic tweets. the best f-score macro results were obtained by the bilstm classification architecture using bert base multilingual word embedding which was selected for the final submission.