Thailand

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.

ACL 2024

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

major voting

weighted tfidf

linear support vector classifier

tf-idf features

n-gram analyzers

multi-label country-level dialect identification

nadi 2024

k-nearest neighbors

random forest

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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.

dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers

This paper presents our submission for the Stance Detection in Arabic Language (StanceEval) 2024 shared task conducted by Team SMASH of the University of Edinburgh. We evaluated the performance of various BERT-based and large language models (LLMs). MARBERT demonstrates superior performance among the BERT-based models, achieving F1 and macro-F1 scores of 0.570 and 0.770, respectively. In contrast, Command~R model outperforms all models with the highest overall F1 score of 0.661 and macro F1 score of 0.820.

SMASH at StanceEval 2024: Prompt Engineering LLMs for Arabic Stance Detection

In NLP, stance detection identifies a writer's position or viewpoint on a particular topic or entity from their text and social media activity, which includes preferences and relationships. Researchers have been exploring techniques and approaches to develop effective stance detection systems. Large language models' latest advancements offer a more effective solution to the stance detection problem. This paper proposes fine-tuning the newly released 8B-parameter Llama 3 model from Meta GenAI for Arabic text stance detection. The proposed method was ranked ninth in the StanceEval 2024 Task on stance detection in Arabic language achieving a Macro average $F_1$ score of 0.7647.

CUFE at StanceEval2024: Arabic Stance Detection with Fine-Tuned Llama-3 Model

Stance detection, an evolving task in natural language processing, involves understanding a writer's perspective on certain topics by analyzing his written text and interactions online, especially on social media platforms. In this paper, we outline our submission to the StanceEval task, leveraging the Mawqif dataset featured in The Second Arabic Natural Language Processing Conference. Our task is to detect writers' stances (Favor, Against, or None) towards three selected topics (COVID-19 vaccine, digital transformation, and women empowerment). We present our approach primarily relying on a contrastive loss ensemble strategy. Our proposed approach achieved an F1-score of 0.8438 and ranked first in the stanceEval 2024 task. The code and checkpoints are available at https://github.com/MBadran2000/Mawqif.git

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

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