Singapore

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.



EMNLP 2023

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

arabic

cross-lingual

zero-shot

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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EMNLP 2022

Welcome!
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.

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.

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).

Qamosy at Arabic Reverse Dictionary shared task: Semi Decoder Architecture for Reverse Dictionary with SBERT Encoder | VIDEO

● Our system, submitted to the Nuanced Arabic Dialect Identification (NADI-23), tackles the first sub-task: Closed Country-level dialect identification.
● We propose a model that is based on an ensemble of layer-wise fine-tuned BERT-based models.
● The proposed model ranked fourth out of sixteen submissions, with an F1-macro score of 85%.

SANA at NADI 2023 shared task: Ensemble of Layer-Wise BERT-based models for Dialectal Arabic Identification

The Holy Qur’an is central to Islam, influencing around two billion Muslims globally, and is known for its linguistic richness and complexity. This article discusses our involvement in the PR task (Task A) of the Qur’an QA 2023 Shared Task. We used two models: one employing the Sentence Transformer and the other using OpenAI’s embeddings for document retrieval. Both models, equipped with a translation feature, help interpret and understand Arabic language queries by translating them, executing the search, and then reverting the results to Arabic. Our results show that incorporating translation functionalities improves the performance in Arabic Question-Answering systems. The model with translation enhancement performed notably better in all metrics compared to the non-translation model.


AHJL at Qur'an QA 2023 Shared Task: Enhancing Passage Retrieval using Sentence Transformer and Translation

The rise of propaganda and disinformation in the digital age has necessitated the development of effective detection methods to combat the spread of deceptive information. In this paper, we present our approach proposed for the ArAIEval shared task: propaganda and disinformation detection in Arabic text. Our system utilized different pre-trained BERT-based models, that make use of prompt-learning based on knowledgeable expansion and prefix-tuning.
The proposed approach secured third place in subtask-1A with a 0.7555 F1-micro score, and second place in subtask-1B with a 0.5658 F1-micro score. However, for subtask-2A & 2B, the proposed system achieved fourth place with an F1-micro score of 0.9040, and 0.8219 respectively. Our findings suggest that prompt-tuning-based & prefix-tuning based models performed better than conventional fine-tuning. Furthermore, using loss-aware class imbalance, improved performance.

rematchka at ArAIEval Shared Task: Prefix-Tuning & Prompt-tuning for Improved Detection of Propaganda and Disinformation in Arabic Social Media Content

In this paper, we describe our participation in the NADI2023 shared task for the classification of Arabic dialects in tweets. For training, evaluation, and testing purposes, a primary dataset comprising tweets from 18 Arab countries is provided, along with three older datasets. The main objective is to develop a model capable of classifying tweets from these 18 countries. We outline our approach, which leverages various machine learning models. Our experiments demonstrate that large language models, particularly Arabertv2-Large, Arabertv2-Base, and CAMeLBERT-Mix DID MADAR, consistently outperform traditional methods such as SVM, XGBOOST, Multinomial Naive Bayes, AdaBoost, and Random Forests

IUNADI at NADI 2023 shared task: Country-level Arabic Dialect Classification in Tweets for the Shared Task NADI 2023

Wikipedia articles are a widely used source of training data for Natural Language Processing (NLP) research, particularly as corpora for low-resource languages like Arabic. However, it is essential to understand the extent to which these corpora reflect the representative contributions of native speakers, especially when many entries in a given language are directly translated from other languages or automatically generated through automated mechanisms. In this paper, we study the performance implications of using inorganic corpora that are not representative of native speakers and are generated through automated techniques such as bot generation or automated template-based translation. The case of the Arabic Wikipedia editions gives a unique case study of this since the Moroccan Arabic Wikipedia edition (ARY) is small but representative, the Egyptian Arabic Wikipedia edition (ARZ) is large but unrepresentative, and the Modern Standard Arabic Wikipedia edition (AR) is both large and more representative. We intrinsically evaluate the performance of two main NLP upstream tasks, namely word representation and language modeling, using word analogy evaluations and fill-mask evaluations using our two newly created datasets: Arab States Analogy Dataset (ASAD) and Masked Arab States Dataset (MASD). We demonstrate that for good NLP performance, we need both large and organic corpora; neither alone is sufficient. We show that producing large corpora through automated means can be a counter-productive, producing models that both perform worse and lack cultural richness and meaningful representation of the Arabic language and its native speakers.

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