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相关论文: MuDRiC: Multi-Dialect Reasoning for Arabic Commons…

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Large language models (LLMs) have the potential of being useful tools that can automate tasks and assist humans. However, these models are more fluent in English and more aligned with Western cultures, norms, and values. Arabic-specific…

计算与语言 · 计算机科学 2025-03-20 Amr Keleg

The latest advancements in machine learning and deep learning have brought forth the concept of semantic similarity, which has proven immensely beneficial in multiple applications and has largely replaced keyword search. However, evaluating…

计算与语言 · 计算机科学 2024-05-31 Ali Mahboub , Muhy Eddin Za'ter , Bashar Al-Rfooh , Yazan Estaitia , Adnan Jaljuli , Asma Hakouz

In this paper, we tackle the Nuanced Arabic Dialect Identification (NADI) shared task (Abdul-Mageed et al., 2021) and demonstrate state-of-the-art results on all of its four subtasks. Tasks are to identify the geographic origin of short…

计算与语言 · 计算机科学 2021-03-02 Badr AlKhamissi , Mohamed Gabr , Muhammad ElNokrashy , Khaled Essam

Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present in Arabic language. Documents…

信息检索 · 计算机科学 2013-02-08 Hanane Froud , Abdelmonaime Lachkar , Said Alaoui Ouatik

With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that support multiple languages. One task of interest is claim…

计算与语言 · 计算机科学 2021-05-19 Tariq Alhindi , Amal Alabdulkarim , Ali Alshehri , Muhammad Abdul-Mageed , Preslav Nakov

Pre-trained Language Models (PLMs) are integral to many modern natural language processing (NLP) systems. Although multilingual models cover a wide range of languages, they often grapple with challenges like high inference costs and a lack…

计算与语言 · 计算机科学 2024-07-19 Murtadha Ahmed , Saghir Alfasly , Bo Wen , Jamaal Qasem , Mohammed Ahmed , Yunfeng Liu

This review explores recent advances in commonsense reasoning and intent detection, two key challenges in natural language understanding. We analyze 28 papers from ACL, EMNLP, and CHI (2020-2025), organizing them by methodology and…

计算与语言 · 计算机科学 2025-06-18 Md Nazmus Sakib

Arabic is one of the most important and growing languages in the world. With the rise of social media platforms such as Twitter, Arabic spoken dialects have become more in use. In this paper, we describe our approach on the NADI Shared Task…

计算与语言 · 计算机科学 2020-11-16 Ahmad Beltagy , Abdelrahman Wael , Omar ElSherief

Large Language Models (LLMs) are the engines driving today's AI agents. The better these models understand human languages, the more natural and user-friendly the interaction with AI becomes, from everyday devices like computers and…

计算与语言 · 计算机科学 2025-11-24 Mohamed Mahdi

Poetry has long been a central art form for Arabic speakers, serving as a powerful medium of expression and cultural identity. While modern Arabic speakers continue to value poetry, existing research on Arabic poetry within Large Language…

Arabic text recognition is a challenging task because of the cursive nature of Arabic writing system, its joint writing scheme, the large number of ligatures and many other challenges. Deep Learning DL models achieved significant progress…

计算机视觉与模式识别 · 计算机科学 2020-09-07 Mohammad Fasha , Bassam Hammo , Nadim Obeid , Jabir Widian

Smooth and effective communication requires the ability to perform latent or explicit commonsense inference. Prior commonsense reasoning benchmarks (such as SocialIQA and CommonsenseQA) mainly focus on the discriminative task of choosing…

计算与语言 · 计算机科学 2021-09-23 Pei Zhou , Karthik Gopalakrishnan , Behnam Hedayatnia , Seokhwan Kim , Jay Pujara , Xiang Ren , Yang Liu , Dilek Hakkani-Tur

Language technologies that accurately model the dynamics of events must perform commonsense reasoning. Existing work evaluating commonsense reasoning focuses on making inferences about common, everyday situations. To instead investigate the…

We present Dolphin, a novel benchmark that addresses the need for a natural language generation (NLG) evaluation framework dedicated to the wide collection of Arabic languages and varieties. The proposed benchmark encompasses a broad range…

计算与语言 · 计算机科学 2023-10-25 El Moatez Billah Nagoudi , AbdelRahim Elmadany , Ahmed El-Shangiti , Muhammad Abdul-Mageed

In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them. We present SICK, a framework that uses commonsense inferences…

计算与语言 · 计算机科学 2022-09-05 Seungone Kim , Se June Joo , Hyungjoo Chae , Chaehyeong Kim , Seung-won Hwang , Jinyoung Yeo

Pre-trained language models (PTLMs) have achieved impressive performance on commonsense inference benchmarks, but their ability to employ commonsense to make robust inferences, which is crucial for effective communications with humans, is…

计算与语言 · 计算机科学 2021-09-13 Pei Zhou , Rahul Khanna , Seyeon Lee , Bill Yuchen Lin , Daniel Ho , Jay Pujara , Xiang Ren

Commonsense question-answering (QA) tasks, in the form of benchmarks, are constantly being introduced for challenging and comparing commonsense QA systems. The benchmarks provide question sets that systems' developers can use to train and…

人工智能 · 计算机科学 2020-12-23 Henrique Santos , Minor Gordon , Zhicheng Liang , Gretchen Forbush , Deborah L. McGuinness

Large Language Models (LLMs) are now integral to numerous industries, increasingly serving as the core reasoning engine for autonomous agents that perform complex tasks through tool-use. While the development of Arabic-native LLMs is…

Automatic readability assessment is relevant to building NLP applications for education, content analysis, and accessibility. However, Arabic readability assessment is a challenging task due to Arabic's morphological richness and limited…

计算与语言 · 计算机科学 2024-07-04 Juan Piñeros Liberato , Bashar Alhafni , Muhamed Al Khalil , Nizar Habash