Thailand

Recognizing the nuanced spectrum of dialectness in Arabic text poses a significant challenge for natural language processing (NLP) tasks. Traditional dialect identification (DI) methods treat the task as binary, overlooking the continuum of dialect variation present in Arabic speech and text. In this paper, we describe our submission to the NADI shared Task of ArabicNLP 2024. We participated in Subtask 2 - ALDi Estimation, which focuses on estimating the Arabic Level of Dialectness (ALDi) for Arabic text, indicating how much it deviates from Modern Standard Arabic (MSA) on a scale from 0 to 1, where 0 means MSA and 1 means high divergence from MSA. We explore diverse training approaches, including contrastive learning, applying a random weighted sampler along with fine-tuning a regression task based on the AraBERT model, after adding a linear and non-linear layer on top of its pooled output. Finally, performing a brute force ensemble strategy increases the performance of our system. Our proposed solution achieved a Root Mean Squared Error (RMSE) of 0.1406, ranking second on the leaderboard.

ACL 2024

AlexUNLP-STM at NADI 2024 shared task: Quantifying the Arabic Dialect Spectrum with Contrastive Learning, Weighted Sampling, and BERT-based Regression Ensemble

root mean squared error (rmse)

aldi estimation

dialectness

ensemble strategy

contrastive learning

workshop paper

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We report the approaches submitted to the NADI 2024 Subtask 1: Multi-label country-level Dialect Identification (MLDID). The core part was to adapt the information from multi-class data for a multi-label dialect classification task. We experimented with supervised and unsupervised strategies to tackle the task in this challenging setting. Under the supervised setup, we used the model trained using NADI 2023 data and devised approaches to convert the multi-class predictions to multi-label by using information from the confusion matrix or using calibrated probabilities. Under unsupervised settings, we used the Arabic-based sentence encoders and multilingual cross-encoders to retrieve similar samples from the training set, considering each test input as a query. The associated labels are then assigned to the input query. We also tried different variations, such as co-occurring dialects derived from the provided development set. We obtained the best validation performance of 48.5% F-score using one of the variations with an unsupervised approach and the same approach yielded the best test result of 43.27% (Ranked 2).

NLP_DI at NADI 2024 shared task: Multi-label Arabic Dialect Classifications with an Unsupervised Cross-Encoder

This study undertakes a comprehensive investigation of transformer-based models to advance Arabic language processing, focusing on two pivotal aspects: the estimation of Arabic Level of Dialectness and dialectal sentence-level machine translation into Modern Standard Arabic. We conducted various evaluations of different sentence transformers across a proposed regression model, showing that the MARBERT transformer-based proposed regression model achieved the best root mean square error of 0.1403 for Arabic Level of Dialectness estimation. In parallel, we developed bi-directional translation models between Modern Standard Arabic and four specific Arabic dialects—Egyptian, Emirati, Jordanian, and Palestinian—by fine-tuning and evaluating different sequence-to-sequence transformers. This approach significantly improved translation quality, achieving a BLEU score of 0.1713. We also enhanced our evaluation capabilities by integrating MSA predictions from the machine translation model into our Arabic Level of Dialectness estimation framework, forming a comprehensive pipeline that not only demonstrates the effectiveness of our methodologies but also establishes a new benchmark in the deployment of advanced Arabic NLP technologies.

ASOS at NADI 2024 shared task: Bridging Dialectness Estimation and MSA Machine Translation for Arabic Language Enhancement

This paper presents the contribution of our dzNLP team to the NADI 2024 shared task, specifically in Subtask 1 - Multi-label Country-level Dialect Identification (MLDID) (Closed Track). We explored various configurations to address the challenge: in Experiment 1, we utilized a union of n-gram analyzers (word, character, character with word boundaries) with different n-gram values; in Experiment 2, we combined a weighted union of Term Frequency-Inverse Document Frequency (TF-IDF) features with various weights; and in Experiment 3, we implemented a weighted major voting scheme using three classifiers: Linear Support Vector Classifier (LSVC), Random Forest (RF), and K-Nearest Neighbors (KNN). Our approach, despite its simplicity and reliance on traditional machine learning techniques, demonstrated competitive performance in terms of accuracy and precision. Notably, we achieved the highest precision score of 63.22% among the participating teams. However, our overall F1 score was approximately 21%, significantly impacted by a low recall rate of 12.87%. This indicates that while our models were highly precise, they struggled to recall a broad range of dialect labels, highlighting a critical area for improvement in handling diverse dialectal variations.

dzNLP at NADI 2024 Shared Task: Multi-Classifier Ensemble with Weighted Voting and TF-IDF Features

This paper describes our submissions to the Multi-label Country-level Dialect Identification subtask of the NADI2024 shared task, organized during the second edition of the ArabicNLP conference. Our submission is based on the ensemble of fine-tuned BERT-based models, after implementing the Similarity-Induced Mono-to-Multi Label Transformation (SIMMT) on the input data. Our submission ranked first with a Macro-Average (MA) F1 score of 50.57%.

ELYADATA at NADI 2024 shared task: Arabic Dialect Identification with Similarity-Induced Mono-to-Multi Label Transformation.

DA-MSA Machine Translation is a recent challenge due to the multitude of Arabic dialects and their variations. In this paper, we present our results within the context of Subtask 3 of the NADI-2024 Shared Task(Abdul- Mageed et al., 2024) that is DA-MSA Machine Translation . We utilized the DIALECTS008 MSA MADAR corpus (Bouamor et al., 2018), the Emi-NADI corpus for the Emirati dialect (Khered et al., 2023), and we augmented the Palestinian and Jordanian datasets based on NADI 2021. Our approach involves develop013 ing sentence-level machine translations from Palestinian, Jordanian, Emirati, and Egyptian dialects to Modern Standard Arabic (MSA).To 016 address this challenge, we fine-tuned mod els such as (Nagoudi et al., 2022)AraT5v2- msa-small, AraT5v2-msa-base, and (Elmadany et al., 2023)AraT5v2-base-1024 to compare their performance. Among these, the AraT5v2- base-1024 model achieved the best accuracy, with a BLEU score of 0.1650 on the develop023 ment set and 0.1746 on the test set.

Alson at NADI 2024 shared task: Alson - A fine-tuned model for Arabic Dialect Translation

LLMs such as GPT-4 and LLaMA excel in multiple natural language processing tasks, however, LLMs face challenges in delivering satisfactory performance on low-resource languages due to limited availability of training data. In this paper, LLaMA-3 with 8 Billion parameters is finetuned to translate among Egyptian, Emirati, Jordanian, Palestinian Arabic dialects, and Modern Standard Arabic (MSA). In the NADI 2024 Task on DA-MSA Machine Translation, the proposed method achieved a BLEU score of 21.44 when it was fine-tuned on the development dataset of the NADI 2024 Task on DA-MSA and a BLEU score of 16.09 when trained when it was fine-tuned using the OSACT dataset.

CUFE at NADI 2024 shared task: Fine-Tuning Llama-3 To Translate From Arabic Dialects To Modern Standard Arabic

$$Recently, there has been a growing interest in analyzing user-generated text to understand opinions expressed on social media. In NLP, this task is known as stance detection, where the goal is to predict whether the writer is in favor, against, or has no opinion on a given topic. Stance detection is crucial for applications such as sentiment analysis, opinion mining, and social media monitoring, as it helps in capturing the nuanced perspectives of users on various subjects. As part of the ArabicNLP 2024 program, we organized the first shared task on Arabic Stance Detection, StanceEval 2024. This initiative aimed to foster advancements in stance detection for the Arabic language, a relatively underrepresented area in Arabic NLP research. This overview paper provides a detailed description of the shared task, covering the dataset, the methodologies used by various teams, and a summary of the results from all participants. We received 28 unique team registrations, and during the testing phase, 16 teams submitted valid entries. The highest classification F-score obtained was 84.38.$$

$StanceEval 2024: The First Arabic Stance Detection Shared Task$

This research explores the effectiveness of using pre-trained language models (PLMs) as feature extractors for Arabic stance detection on social media, focusing on topics like women empowerment, COVID-19 vaccination, and digital transformation. By leveraging sentence transformers to extract embeddings and incorporating aggregation architectures on top of BERT, we aim to achieve high performance without the computational expense of fine-tuning. Our approach demonstrates significant resource and time savings while maintaining competitive performance, scoring an F1-score of 78.62 on the test set. This study highlights the potential of PLMs in enhancing stance detection in Arabic social media analysis, offering a resource-efficient alternative to traditional fine-tuning methods.

Team_Zero at StanceEval2024: Frozen PLMs for Arabic Stance Detection

As part of our study, we worked on three tasks: stance detection, sarcasm detection and senti- ment analysis using fine-tuning techniques on BERT-based models. Fine-tuning parameters were carefully adjusted over multiple iterations to maximize model performance. The three tasks are essential in the field of natural lan- guage processing (NLP) and present unique challenges. Stance detection is a critical task aimed at identifying a writer’s stances or view- points in relation to a topic. Sarcasm detection seeks to spot sarcastic expressions, while senti- ment analysis determines the attitude expressed in a text. After numerous experiments, we iden- tified Arabert-twitter as the model offering the best performance for all three tasks. In particu- lar, it achieves a macro F-score of 78.08% for stance detection, a macro F1-score of 59.51% for sarcasm detection and a macro F1-score of 64.57% for sentiment detection. . Our source code is available at https:// github.com/MezghaniAmal/Mawqif

ANLP RG at StanceEval2024: Comparative Evaluation of Stance, Sentiment and Sarcasm Detection

This study compares Term Frequency-Inverse Document Frequency (TF-IDF) features with Sentence Transformers for detecting writers' stances—favorable, opposing, or neutral—towards three significant topics: COVID-19 vaccine, digital transformation, and women empowerment. Through empirical evaluation, we demonstrate that Sentence Transformers outperform TF-IDF features across various experimental setups. Our team, dzStance, participated in a stance detection competition, achieving the 13th position (74.91%) among 15 teams in Women Empowerment, 10th (73.43%) in COVID Vaccine, and 12th (66.97%) in Digital Transformation. Overall, our team's performance ranked 13th (71.77%) among all participants. Notably, our approach achieved promising F1-scores, highlighting its effectiveness in identifying writers' stances on diverse topics. These results underscore the potential of Sentence Transformers to enhance stance detection models for addressing critical societal issues.

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