Singapore

This paper introduces a comprehensive system designed to address two natural language processing tasks: Passage Retrieval (Task A) and Reading Comprehension (Task B), applied to datasets related to the Holy Qur’an. Task A was treated as a measurement of a textual similarity problem where the system leverages OpenAI’s &quot;text-embedding-ada-002&quot; embedding model to transform textual content into
numerical representations, with cosine similarity serving as the proximity metric. Task B focuses on the extraction of answers from Qur’anic passages, employing the Generative Pre-trained Transformer-4 (GPT-4) language model. In Task A, the system is evaluated using the Mean Average Precision (MAP) metric, achieving MAP scores of 0.109438 and 0.06426543057 on the development and test datasets with an optimal similarity threshold set at 0.85. Task B evaluation employs partial Average Precision (pAP), where our system surpasses a baseline whole-passage retriever with pAP scores of 0.470 and 0.5393130538 on the development and test datasets, respectively.

EMNLP 2023

Al-Jawaab at Qur’an QA 2023 Shared Task: Exploring Embeddings and GPT Models for Passage Retrieval and Reading Comprehension | VIDEO

reading comprehensive

holy qur’an

gpt-4

embeddings

passage retrieval

This paper introduces a comprehensive system designed to address two natural language processing tasks: Passage Retrieval (Task A) and Reading Comprehension (Task B), applied to datasets related to the Holy Qur’an. Task A was treated as a measurement of a textual similarity problem where the system leverages OpenAI’s "text-embedding-ada-002" embedding model to transform textual content into
numerical representations, with cosine similarity serving as the proximity metric. Task B focuses on the extraction of answers from Qur’anic passages, employing the Generative Pre-trained Transformer-4 (GPT-4) language model. In Task A, the system is evaluated using the Mean Average Precision (MAP) metric, achieving MAP scores of 0.109438 and 0.06426543057 on the development and test datasets with an optimal similarity threshold set at 0.85. Task B evaluation employs partial Average Precision (pAP), where our system surpasses a baseline whole-passage retriever with pAP scores of 0.470 and 0.5393130538 on the development and test datasets, respectively.

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.

In this paper we present our approach towards Arabic Dialect identification which was part of the 
The Fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023). We tested several techniques to identify Arabic dialects. We obtained the best result by fine-tuning the pre-trained MARBERTv2 model with a modified training dataset. The training set was expanded by sorting tweets based on dialects, concatenating every two adjacent tweets, and adding them to the original dataset as new tweets. We achieved 82.87 on F1 score and we were at the seventh position among 16 participants.

UoT at NADI 2023 shared task: Automatic Arabic Dialect Identification is Made Possible

We present our approach to tackle Qur’an QA 2023 shared tasks A and B. To address the challenge of low-resourced training data, we rely on transfer learning together with a voting ensemble to improve prediction stability across multiple runs. Additionally, we employ different architectures and learning mechanisms for a range of Arabic pre-trained transformer-based models for both tasks. To identify unanswerable questions, we propose using a thresholding mechanism. Our top-performing systems greatly surpass the baseline performance on the hidden split, achieving a MAP score of 25.05% for task A and a partial Average Precision (pAP) of 57.11% for task B.

TCE at Qur'an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur'anic QA | VIDEO

Team UniManc developed models for the two subtasks of Machine Translation MT from dialectical Arabic to Modern Standard Arabic (MSA) in the Nuanced Arabic Dialect Identification (NADI) 2023 shared task. Based on the results of the official NADI 2023 evaluation, our Independent Regional (I-R) AraT5v2 model obtained an overall BLEU score of 14.76, ranking first in the Closed Dialect-to-MSA MT subtask (subtask 2). Moreover, in the Open Dialect-to-MSA MT subtask (subtask 3), our Joint Regional (J-R) AraT5v2 model also ranked first, obtaining an overall BLEU score of 21.10.

UniManc at NADI 2023 Shared Task: A Comparison of Various T5-based Models for Translating Arabic Dialectical Text to Modern Standard Arabic

Social media has significantly amplified the dissemination of misinformation. Researchers have employed natural language processing and machine learning techniques to identify and categorize false information on these platforms. While there is a well-established body of research on detecting fake news in English and Latin languages, the study of Arabic fake news detection remains limited. This paper describes the methods used to tackle the challenges of the ArAIEval shared Task 2023. We conducted experiments with both monolingual Arabic and multi-lingual pre-trained Language Models (LM). We found that the monolingual Arabic models outperformed in all four subtasks. Additionally, we explored a novel lossless compression method, which, while not surpassing pretrained LM performance, presents an intriguing avenue for future experimentation to achieve comparable results in a more efficient and rapid manner.

Itri Amigos at ArAIEval Shared Task: Transformer vs. Compression-Based Models for Persuasion Techniques and Disinformation Detection | VIDEO

Detecting offensive language in under-resourced languages presents a significant real-world challenge for social media platforms. This paper is the first work focused on the issue of offensive language detection in Arabizi, an under-explored topic in an under-resourced form of Arabic. For the first time, a comprehensive and critical overview of the existing work on the topic is presented. In addition, we carry out experiments using different BERT-like models and show the feasibility of detecting offensive language in Arabizi with high accuracy. Throughout a thorough analysis of results, we emphasize the complexities introduced by dialect variations and out-of-domain generalization. We use in our experiments a dataset that we have constructed by leveraging existing, albeit limited, resources. To facilitate further research, we make this dataset publicly accessible to the research community.

Offensive Language Detection in Arabizi

To enhance persuasion detection, we investigate the use of multilingual systems on Arabic data by conducting a total of 22 experiments using baselines, multilingual, and monolingual language transformers. Our aim is to provide a comprehensive evaluation of the various systems employed throughout this task, with the ultimate goal of comparing their performance and identifying the most effective approach. Our empirical analysis shows that ReDASPersuasion system performs best when combined with multilingual "XLM-RoBERTa" and monolingual pre-trained transformers on Arabic dialects like "CAMeLBERT-DA SA" depending on the NLP classification task.

ReDASPersuasion at ArAIEval Shared Task: Multilingual and Monolingual Models For Arabic Persuasion Detection

HTE at ArAIEval Shared Task: Integrating Content Type Information in Binary Persuasive Technique Detection

ANLP-RG at NADI 2023 shared task: Machine Translation of Arabic Dialects: A Comparative Study of Transformer Models

ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text

Motivated by the need for intelligent question answering (QA) systems on the Holy Qur'an and the success of the first Qur'an Question Answering shared task (Qur'an QA 2022 at OSACT 2022), we have organized the second version at ArabicNLP 2023. The Qur'an QA 2023 is composed of two sub-tasks: the passage retrieval (PR) task and the machine reading comprehension (MRC) task. The main aim of the shared task is to encourage state-of-the-art research on Arabic PR and MRC on the Holy Qur'an. Our shared task has attracted 9 teams to submit 22 runs for the PR task, and 6 teams to submit 17 runs for the MRC task. In this paper, we present an overview of the task and provide an outline of the approaches employed by the participating teams in both sub-tasks.

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Al-Jawaab at Qur’an QA 2023 Shared Task: Exploring Embeddings and GPT Models for Passage Retrieval and Reading Comprehension | VIDEO

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UoT at NADI 2023 shared task: Automatic Arabic Dialect Identification is Made Possible

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