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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).

EMNLP 2023

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

sentence transformers

reverse dictionary

arabic nlp

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**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.

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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

● 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.

Performance Implications of Using Unrepresentative Corpora in Arabic Natural Language Processing

DialectNLU at NADI 2023 Shared Task: Transformer Based Multitask Approach Jointly Integrating Dialect and Machine Translation Tasks in Arabic

Dialect identification systems play a significant role in various fields and applications as in speech and language technologies, facilitating language education, supporting sociolinguistic research, preserving linguistic diversity, and enhancing text-to-speech systems. In this paper, we provide our findings and results in the NADI 2023 shared task for country-level dialect identification and machine translation (MT) from dialect to MSA. The proposed models achieved an F1-score of 86.18 at the dialect identification task, securing second place in the first subtask. Whereas for the machine translation task, the submitted model achieved a BLEU score of 11.37 securing fourth and third place in the second and third subtasks. The proposed model utilizes parameter-efficient training methods which achieves better performance when compared to conventional fine-tuning during the experimentation phase.

rematchka at NADI 2023 shared task: Parameter Efficient tuning for Dialect Identification and Dialect Machine Translation

Through out the presentation, I highlight the main problems we tackled through out our paper titled "AAST-NLP at ArAIEval Shared Task: Tackling Persuasion Technique and Disinformation Detection using Pre-Trained Language Models On Imbalanced Datasets", Our research work mainly focuses on data imbalance in the provided datasets and ways used to tackle them.

AAST-NLP at ArAIEval Shared Task: Tackling Persuasion technique and Disinformation Detection using Pre-Trained Language Models On Imbalanced Datasets

This paper outlines a methodology aimed at combating disinformation in Arabic social media, a strategy that secured a first-place finish in tasks 2A and 2B at the ArAIEval shared task during the ArabicNLP 2023 workshop. Our team developed a hyperparameter-optimized pipeline centered around BERT-based models for the Arabic language, enhanced by a soft-voting ensemble strategy. Subsequent evaluation on the test dataset reveals that ensembles, although generally resilient, do not always outperform individual models. The primary contributions of this paper are its multifaceted strategy, which led to winning solutions for both binary (2A) and multiclass (2B) disinformation classification tasks.

DetectiveRedasers at ArAIEval Shared Task: Leveraging Transformer Ensembles for Arabic Deception Detection

We present ArTrivia, a new Arabic question answering dataset consisting of more than 10,000 question-answer pairs along with relevant passages, covering a wide range of 18 diverse topics in Arabic. We created our dataset using a newly proposed pipeline that leverages diverse structured data sources from Arabic Wikipedia. Moreover, we conducted a comprehensive statistical analysis of ArTrivia and assessed the performance of each component in our pipeline. Additionally, we compared the performance of ArTrivia against the existing TyDi QA dataset using various experimental setups. Our analysis highlights the significance of often overlooked aspects in dataset creation, such as answer normalization, in enhancing the quality of QA datasets. Our evaluation also shows that ArTrivia presents more challenging and out-of-distribution questions to TyDi, raising questions about the feasibility of using ArTrivia as a complementary dataset to TyDi.

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Next from EMNLP 2023

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

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