Singapore

This paper provides a systematic analysis and comparison of the performance of state-of-the-art models on the task of fine-grained Arabic dialect identification using the MADAR parallel corpus. We test approaches based on pre-trained transformer language models in addition to Naive Bayes models with a rich set of various features. Through a comprehensive data- and error analysis, we provide
valuable insights into the strengths and weaknesses of both approaches. We discuss which dialects are more challenging to differentiate, and identify potential sources of errors. 
Our analysis reveals an important problem with identical sentences across dialect classes in the test set of the MADAR-26 corpus, which may confuse any classifier. We also show that none of the tested approaches captures the subtle distinctions between closely related dialects.

EMNLP 2023

Arabic dialect identification: An in-depth error analysis on the MADAR parallel corpus | VIDEO

dialect identification

data analysis

error analysis

workshop paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detection

Most recent models for Arabic topic classification leveraged fine-tuning existing pre-trained transformer models and targeted a limited number of categories. More recently, advances in automated ML and generative models introduced novel potentials for the task. While these approaches work for English, it is a question of
whether they perform well for low-resourced languages; Arabic in particular. This paper presents (i) ArBNTopic; a novel Arabic dataset with an extended 14-topic class set covering modern books from social sciences and humanities along with newspaper articles, and (ii) a set of topic classifiers built from it. We fine-tuned
an open LLM model to build ArGTC. We compared its performance against the best models built with Vertex AI (Google), AutoML(H2O), and AutoTrain(HuggingFace). ArGTC outperformed the VertexAi and AutoML models and was reasonably similar to the AutoTrain model.

Arabic Topic Classification in the Generative and AutoML Era

In this paper, we highlight our approach for the "Arabic AI Tasks Evaluation ArAiEval)
Shared Task 2023". We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. Detection of persuasion techniques and disinformation has become imperative to avoid distortion of authentic information. The tasks use multigenre snippets of tweets and news articles for the given binary classification problem. We experiment with several transformer-based models that were pre-trained on the Arabic language. We fine-tune these state-of-the-art models on the provided dataset. Ensembling is employed to enhance the performance of the systems. We achieved a micro F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task 2-A (7th rank on the leaderboard) respectively.

Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space - Transformer Ensemble Models Tackling Deception and Persuasion

In this paper, we present our approach for the "Nuanced Arabic Dialect Identification (NADI) Shared Task 2023". We highlight our methodology for subtask 1 which deals with country level dialect identification. Recognizing dialects plays an instrumental role in enhancing the performance of various downstream NLP tasks such as speech recognition and translation. The task uses the Twitter dataset (TWT-2023) that encompasses 18 dialects for the multi-class classification problem. Numerous transformer based models, pre-trained on Arabic language,
are employed for identifying country-level dialects. We fine-tune these state-of-the art models on the provided dataset. The ensembling method is leveraged to yield improved performance of the system. We achieved an F1-score of 76.65 (11th rank on the leaderboard) on the test dataset

Mavericks at NADI 2023 Shared Task: Unravelling Regional Nuances through Dialect Identification using Transformer-based Approach | VIDEO

Numerous languages exhibit shared characteristics, be it in their scripts or morphological features. For instance, Arabic and Persian utilize the same script, whereas Arabic and Russian both belong to the fusional language category. The question arises: Do such common traits influence language comprehension across diverse linguistic backgrounds? This study explores the possibility of transferring comprehension skills across languages to Arabic in a zero-shot scenario. Specifically, we demonstrate that training language models on other languages can enhance comprehension of Arabic, as evidenced by our evaluations in three key tasks: natural language inference, question answering, and named entity recognition. Our experiments reveal that certain morphologically rich languages (MRLs), such as Russian, display similarities to Arabic when assessed in a zero-shot context, particularly in tasks like question answering and natural language inference. However, this similarity is less pronounced in tasks like named entity recognition.



Investigating Zero-shot Cross-lingual Language Understanding for Arabic | VIDEO

This work explores Arabic disinformation identification,
a crucial task in natural language
processing, using a state-of-the-art NLP model.
We highlight the performance of our system
model against baseline models, including multilingual
and Arabic-specific ones, and showcase
the effectiveness of domain-specific pre-trained
models. This work advocates for the adoption
of tailored pre-trained models in NLP, emphasizing
their significance in understanding diverse
languages. By merging advanced NLP
techniques with domain-specific pre-training, it
advances Arabic disinformation identification.

PD-AR at ArAIEval Shared Task: A BERT-Centric Approach to Tackle Arabic Disinformation


Arabic dialects have extensive global usage owing to their significance and the vast number of Arabic speakers. However, technological progress and globalization are leading to significant transformations within Arabic dialects. They acquire new characteristics involving novel vocabulary and integrate linguistic elements from diverse dialects. Consequently, sentiment analysis of these dialects is becoming more challenging. This study categorizes dialects among 18 countries, as introduced by the Nuanced Arabic Dialect Identification (NADI) shared task competition. The study approach incorporates the utilization of the MARBERT and MARBERT v2 models with fine-tuning processes. The findings reveal that the most effective model is achieved by applying averaging and concatenation to the hidden layers of MARBERT v2 and feeding the resulting output into convolutional layers. Furthermore, employing the ensemble method on various methods enhances the model's performance. Our system secures the 6th position among the top performers in the First subtask, achieving an F1 score of 83.73\%.


ISL-AAST at NADI 2023 shared task: Enhancing Arabic Dialect Identification in the Era of Globalization and Technological Progress | VIDEO

LANS: Large-scale Arabic News Summarization Corpus

The rapid proliferation of disinformation
through social media has become one of the
most dangerous means to deceive and influence
people’s thoughts, viewpoints, or behaviors
due to social media’s facilities, such as
rapid access, lower cost, and ease of use. Disinformation
can spread through social media
in different ways, such as fake news stories,
doctored images or videos, deceptive data, and
even conspiracy theories, thus making detecting
disinformation challenging. This paper is
a part of participation in the ArAIEval competition
that relate to disinformation detection.
This work evaluated four models: MARBERT,
the proposed ensemble model, and two tests
over GPT-4 (zero-shot and Few-shot). GPT-4
achieved micro-F1 79.01% while the ensemble
method obtained 76.83%. Despite no improvement
in the micro-F1 score on the dev dataset
using the ensemble approach, we still used it for
the test dataset predictions. We believed that
merging different classifiers might enhance the
system’s prediction accuracy.

AraDetector at ArAIEval Shared Task: An Ensemble of Arabic-specific pre-trained BERT and GPT-4 for Arabic Disinformation Detection

A reverse dictionary takes a descriptive phrase of a particular concept and returns words with definitions that align with that phrase. While many reverse dictionaries cater to languages such as English and are readily available online or have been developed by researchers, there is a notable lack of similar resources for the Arabic language. This paper describes our participation in the Arabic Reverse Dictionary shared task. Our proposed method consists of two main steps: First, we convert word definitions into multidimensional vectors. Then, we train these encoded vectors using the SemiDecoder model for our target task. Our system secured
2nd place based on the Rank metric for both embeddings (Electra and Sgns).

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