United Arab Emirates

Arabic has a wide range of dialects. Dialect is the language variation of a specific community. In this paper, we show the models we created to participate in the third Nuanced Arabic Dialect Identification (NADI) shared task (Subtask 1) that involves developing a system to classify a tweet into a country-level dialect. We utilized a number of machine learning techniques as well as deep learning transformer-based models. For the machine learning approach, we build an ensemble classifier of various machine learning models. In our deep learning approach, we consider bidirectional LSTM model and AraBERT pretrained model. The results demonstrate that the deep learning approach performs noticeably better than the other machine learning approaches with 68.7% accuracy on the development set.

EMNLP 2022

Arabic dialect identification using machine learning and transformer-based models: Submission to the NADI 2022 Shared Task

arabic dialect identification

arabic nlp

machine learning

deep learning

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EMNLP 2023

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

In this paper, we describe our systems submitted to the NADI Subtask 1: country-wise dialect classifications. We designed two types of solutions. The first type is convolutional neural network CNN) classifiers trained on subword segments of optimized lengths. The second type is fine-tuned classifiers with BERT-based language specific pre-trained models. To deal with the missing dialects in one of the test sets, we experimented with binary classifiers, analyzing the predicted probability distribution patterns and comparing them with the development set patterns. The better performing approach on the development set was fine-tuning language specific pre-trained model (best F-score 26.59%). On the test set, on the other hand, we obtained the best performance with the CNN model trained on subword tokens obtained with a Unigram model (the best F-score 26.12%). Re-training models on samples of training data simulating missing dialects gave the maximum performance on the test set version with a number of dialects lesser than the training set (F-score 16.44%)

NLP_DI at NADI Shared Task Subtask-1: Sub-word Level Convolutional Neural Models and Pre-trained Binary Classifiers for Dialect Identification

This paper describes the systems submitted by BFCAI team to Nuanced Arabic Dialect Identification (NADI) shared task 2022. Dialect identification task aims at detecting the source variant of a given text or speech segment automatically. There are two subtasks in NADI 2022, the first subtask for country-level identification and the second subtask for sentiment analysis. Our team participated in the first subtask. The proposed systems use Term Frequency Inverse/Document Frequency and word embeddings as vectorization models. Different machine learning algorithms have been used as classifiers. The proposed systems have been tested on two test sets: Test-A and Test-B. The proposed models achieved Macro-f1 score of 21.25% and 9.71% for Test-A and Test-B set respectively. On other hand, the best-performed submitted system achieved Macro-f1 score of 36.48% and 18.95% for Test-A and Test-B set respectively.

Word Representation Models for Arabic Dialect Identification

In this paper, we describe the approaches we developed for the Nuanced Arabic Dialect Identification (NADI) 2022 shared task, which consists of two subtasks: the identification of country-level Arabic dialects and sentiment analysis. Our team, UniManc, developed approaches to the two subtasks which are underpinned by the same model: a pre-trained MARBERT language model. For Subtask 1,  we applied undersampling to create versions of the training data with a balanced distribution across classes. For Subtask 2, we further trained the original MARBERT model for the masked language modelling objective using a NADI-provided dataset of unlabelled Arabic tweets. For each of the subtasks, a MARBERT model was fine-tuned for sequence classification, using different values for hyperparameters such as seed and learning rate. This resulted in multiple model variants, which formed the basis of an ensemble model for each subtask. Based on the official NADI evaluation, our ensemble model obtained a macro-F1-score of 26.863, ranking second overall in the first subtask. In the second subtask, our ensemble model also ranked second, obtaining a macro-F1-PN score (macro-averaged F1-score over the Positive and Negative classes) of 73.544.

Building an Ensemble of Transformer Models for Arabic Dialect Classification and Sentiment Analysis

Addressing the correct gender in generative tasks (e.g., Machine Translation) has been an overlooked issue in the Arabic NLP. However, the recent introduction of the Arabic Parallel Gender Corpus (APGC) dataset has established new baselines for the Arabic Gender Rewriting task. To address the Gender Rewriting task, we first pre-train our new Seq2Seq ArabicT5 model on a 17GB of Arabic Corpora. Then, we continue pre-training our ArabicT5 model on the APGC dataset using a newly proposed method. Our evaluation shows that our ArabicT5 model, when trained on the APGC dataset, achieved competitive results against existing state-of-the-art methods. In addition, our ArabicT5 model shows better results on the APGC dataset compared to other Arabic and multilingual T5 models.

Generative Approach for Gender-Rewriting Task with ArabicT5

This paper presents the approach taken for the shared task on Propaganda Detection in Arabic at the Seventh Arabic Natural Language Processing Workshop (WANLP 2022). We participated in Sub-task 1 where the text of a tweet is provided, and the goal is to identify the different propaganda techniques used in it. This problem belongs to multi-label classification. For our solution, we approached leveraging different transformer based pre-trained language models with fine-tuning to solve this problem. We found that MARBERTv2 outperforms in terms of performance where F1-macro is 0.08175 and F1-micro is 0.61116 compared to other language models that we considered. Our method achieved rank 4 in the testing phase of the challenge.

AraProp at WANLP 2022 Shared Task: Leveraging Pre-Trained Language Models for Arabic Propaganda Detection

Propaganda and the spreading of fake news through social media have become a serious problem in recent years. In this paper we present our approach for the shared task on propaganda detection in Arabic in which the goal is to identify propaganda techniques in the Arabic social media text. We propose a semantic similarity detection model to compare text in the test set with the sentences in the train set to find the most similar instances. The label of the target text is obtained from the most similar texts in the train set. The proposed model obtained the micro F1 score of 0.494 on the text data set.

TUB at WANLP22 Shared Task: Using Semantic Similarity for Propaganda Detection in Arabic

The spread of propaganda through the internet has increased drastically over the past years. Lately, propaganda detection has started gaining importance because of the negative impact it has on society. In this work, we describe our approach for the WANLP 2022 shared task which handles the task of propaganda detection in a multi-label setting. The task demands the model to label the given text as having one or more types of propaganda techniques. There are a total of 21 propaganda techniques to be detected. We show that an ensemble of five models performs the best on the task, scoring a micro-F1 score of 59.73%. We also conduct comprehensive ablations and propose various future directions for this work.

ChavanKane at WANLP 2022 Shared Task: Large Language Models for Multi-label Propaganda Detection

Nowadays, the rapid dissemination of data on digital platforms has resulted in the emergence of information pollution and data contamination, specifically mis-information, mal-information, dis-information, fake news, and various types of propaganda. These topics are now posing a serious threat to the online digital realm, posing numerous challenges to social media platforms and governments around the world. In this article, we propose a propaganda detection model based on the transformer-based model AraBERT, with the objective of using this framework to detect propagandistic content in the Arabic social media text scene, well with purpose of making online Arabic news and media consumption healthier and safer. Given the dataset, our results are relatively encouraging, indicating a huge potential for this line of approaches in Arabic online news text NLP.

AraBERT Model for Propaganda Detection

In today's time, online users are regularly exposed to media posts that are propagandistic. Several strategies have been developed to promote safer media consumption in Arabic to combat this. However, there is a limited available multilabel annotated social media dataset. In this work, we have used a pre-trained AraBERT twitter-base model on an expanded train data via data augmentation. Our team CNLP-NITS-PP, has achieved the third rank in subtask 1 at WANLP-2022, for propaganda detection in Arabic (shared task) in terms of micro-F1 score of 0.602.

CNLP-NITS-PP at WANLP 2022 Shared Task: Propaganda Detection in Arabic using Data Augmentation and AraBERT Pre-trained Model

This paper presents the system developed by the NGU_CNLP team for addressing the shared task on Propaganda Detection in Arabic at WANLP 2022. The team participated in the shared tasks' two sub-tasks which are: 1) Propaganda technique identification in text and 2) Propaganda technique span identification. In the first sub-task, the goal is to detect all employed propaganda techniques in some given piece of text out of a possible 17 different techniques or to detect that no propaganda technique is being used in that piece of text. As such, this first sub-task is a multi-label classification problem with a pool of 18 possible labels. Subtask 2 extends sub-task 1, by requiring the identification of the exact text span in which a propaganda technique was employed, making it a sequence labeling problem. For task 1, a combination of a data augmentation strategy coupled with an enabled transformer-based model comprised our classification model. This classification model ranked first amongst the 14 systems participating in this subtask. For sub-task two, a transfer learning model was adopted. The system ranked third among the 3 different models that participated in this subtask.

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