SQU - CS @ NADI 2022: Dialectal A rabic Identification using One-vs-One Classification with TF - IDF Weights Computed on Character n-grams (2022)

Abstract: In this paper, I present an approach using one-vs-one classification scheme with TF-IDF term weighting on character n-grams for identifying Arabic dialects used in social media. The scheme was evaluated in the context of the third Nuanced Arabic Dialect Identification (NADI 2022) shared task for identifying Arabic dialects used in Twitter messages. The approach was implemented with logistic regression loss and trained using stochastic gradient decent (SGD) algorithm. This simple method achieved a macro F1 score of 22.89% and 10.83% on TEST A and TEST B, respectively, in comparison to an approach based on AraBERT pretrained transformer model which achieved a macro F1 score of 30.01% and 14.84%, respectively. My submission based on AraBERT scored a macro F1 average of 22.42% and was ranked 10 out of the 19 teams who participated in the task.

## introduction arabic is well known for its rich morphology and complex system of inflectional forms @xcite . while a word in english may have few inflections, a word in arabic contains many more inflectional forms depending on tense, number, person, mood, gender and voice @xcite . arabs mostly communicate informally using a continuum of dialects that vary from the east in the arabian peninsula to the west in the north african region. these dialects add another layer of complexity since they differ at the phonological, morphological, lexical and syntactic levels @xcite . despite dialects are predominantly used in spoken form, heavy usage of the written form is becoming very popular especially in social media platforms @xcite . most of past research on arabic natural language processing (anlp) have mainly focused on modern standard arabic (msa), the formal language of communication used in the arab world. however, recently, arabic dialects have gained more attention by researchers especially the egyptian dialect @xcite . research on arabic dialects involved improving parts-of-speech tagging @xcite , named entity recognition @xcite , parsing & grammar @xcite and machine translation @xcite . one key finding is that higher-level language tasks on arabic dialects benefit substantially from the application of low-level pre-processing techniques that focus on better segmentation and word morphology analysis @xcite . arabic is considered a low-resource language when compared to other languages @xcite . this makes it challenging to utilize preexisting approaches based on supervised machine learning @xcite . recent works have focused on the use of few-shot or zeroshot learning techniques for arabic dialects with promising results @xcite . the subtask of identifying arabic dialect at the country-level was conducted as part of the third nuanced arabic dialect identification shared task: nadi 2022 @xcite . similar subtask was organized in prior years during nadi 2020 @xcite and nadi 2021 (abdul-mageed et al., 2021) shared tasks. past attempts used a variety of approaches ranging from classical machine learning, to ensemblebased classification, and deep learning mutli-task transformer-based neural networks. the best performing methods reported for this subtask utilized the transformer-based models trained with multitask prediction @xcite . the current paper describes an approach based on one-vs-one classifiers trained with tf-idf term weights on character n-grams for identifying arabic dialects. the motivation for this approach is the success of recent methods that exploit subword units for learning as opposed to the individual word tokens @xcite . the subword units representation work better in practice especially for arabic and help reduce out-of-vocabulary (oov) tokens; a common problem in natural language processing tasks. the proposed approach can be used as a baseline performance on the task of arabic dialect identification. ## data shared task organizers have prepared and distributed two datasets with country-level labels for the task participants that can be utilized for system development as shown in ## results ## conclusion in this paper, i presented my attempt to identify country-level arabic dialects used in twitter messages. the approach based on simple one-vs-one classifiers using logistic regression loss showed good baseline performance on the testing sets for the shared task in comparison to bert-based transformer model (arabert) that was pretrained on 60 million arabic tweets.