A Dialogue-System Using a Qur’anic Ontology

Fairouz, Bendjamaa; Taleb, Nora

doi:10.1109/edis49545.2020.9296437

Cited by 5 publications

(5 citation statements)

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“…The alignment system mechanism with parallel corpus [22], But earlier data is prepared, it was aligned. As clarified by [23].…”

Section: Dataset Labelling Phasementioning

confidence: 99%

Development the Dataset for Automatic Translation System

Ghanem

2023

Journal of Al-Azhar University Engineering Sector

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The Automatic translation systems (ATS) for translation text have extent widely in recent years. The ARS developed to correct several types of text errors explained by the Mossop's prototype such as spelling, typographical, syntactic, semantic, word, and formal ones. The ARS need a large amount of data training in its forms. There is a shortage in German-Arabic datasets for translation and revision purposes. Building dataset is the most time-consuming and the most important part of the text translation process. We make an effort to analyze and work on this large amount of data Sentences, and the form of text free dataset on the ARS, most efforts focus on German and Arabic data. Despite the increase in the number of Arabic, users and the increase in Arabic content on ARS. Therefore, in this paper, Arabic dataset built to use in text translation purpose. This research offers the German-Arabic dataset from the Taxonomy of errors in postediting text for growth the ARS. Our dataset gathered from A Game of Throne saga in German (GR) and Arabic (AR) saga. Our dataset consists of 65,000 bilingual sentences collected from Text. The most significant penalties of this research were the Mossop's prototype terminates to explain all errors; and the prototype had to be lengthy in demand to include the Consistency. Finally, human evaluators were employed to grade the quality of ATS outputs and to revision them. We used a Rapid Miner tool to evaluate the performance of our dataset, the dataset accuracy of 95.12%.

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“…The alignment system mechanism with parallel corpus [22], But earlier data is prepared, it was aligned. As clarified by [23].…”

Section: Dataset Labelling Phasementioning

confidence: 99%

Development the Dataset for Automatic Translation System

Ghanem

2023

Journal of Al-Azhar University Engineering Sector

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“…Another recent work facilitated the search for Islamicrelated information in an interactive way [55]. In this study, F. Bendjamaa et al proposed an Arabic DS based on a preexisting Quranic ontology.…”

Section: Task-oriented Dialogue Systemsmentioning

confidence: 99%

Recent Developments in Arabic Conversational AI: A Literature Review

Fuad

Al-Yahya

2022

IEEE Access

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Conversational AI is one of the most active research areas in AI, and it has gained more attention from academia as well as industry. Given recent advancements in several conversational AI systems in addition to the availability of several datasets, the aim of this study is to explore the landscape of Arabic text-based conversational AI systems. In this work, we provide a thorough review of recent Arabic conversational AI systems. We group them into three categories based on their functionality: (1) questionanswering (QA) systems, (2) task-oriented dialogue systems (DS), and (3) chatbots. Furthermore, we describe the common datasets used in building and evaluating conversational AI systems in Arabic. Few surveys have targeted the conversational AI field for the Arabic language, and we aim to cover this gap with this study. Our contribution focuses on reviewing and analyzing the literature in the field and highlighting future research directions towards human-like conversational AI systems in Arabic.

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“…Most of the research on Arabic task-oriented DS focuses on achieving specific tasks, such as intent classification [34][35][36] and entity classification [34]. However, there some attempts to build task-oriented DS have investigated specific domains, including home automation [34], flight bookings [37], education [38][39][40], hotel reservations [41], and Islamic knowledge enquires [42]. Some Arabic task-oriented DS have been designed to specifically serve the Arabic dialects (e.g., OlloBot [43] and Nabiha [44]).…”

Section: Arabic Task-oriented Dialogue Systemsmentioning

confidence: 99%

“…Some Arabic task-oriented DS have been designed to specifically serve the Arabic dialects (e.g., OlloBot [43] and Nabiha [44]). However, this review excludes some of these studies because they are categorized as chatbots rather than task-oriented DS because their system design does not follow a task-oriented DS structure [39,40,[42][43][44].…”

Section: Arabic Task-oriented Dialogue Systemsmentioning

confidence: 99%

AraConv: Developing an Arabic Task-Oriented Dialogue System Using Multi-Lingual Transformer Model mT5

Fuad

Al-Yahya

2022

Applied Sciences

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Task-oriented dialogue systems (DS) are designed to help users perform daily activities using natural language. Task-oriented DS for English language have demonstrated promising performance outcomes; however, developing such systems to support Arabic remains a challenge. This challenge is mainly due to the lack of Arabic dialogue datasets. This study introduces the first Arabic end-to-end generative model for task-oriented DS (AraConv), which uses the multi-lingual transformer model mT5 with different settings. We also present an Arabic dialogue dataset (Arabic-TOD) and used it to train and test the proposed AraConv model. The results obtained are reasonable compared to those reported in the studies of English and Chinese using the same mono-lingual settings. To avoid problems associated with a small training dataset and to improve the AraConv model’s results, we suggest joint-training, in which the model is jointly trained on Arabic dialogue data and data from one or two high-resource languages such as English and Chinese. The findings indicate the AraConv model performed better in the joint-training setting than in the mono-lingual setting. The results obtained from AraConv on the Arabic dialogue dataset provide a baseline for other researchers to build robust end-to-end Arabic task-oriented DS that can engage with complex scenarios.

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A Dialogue-System Using a Qur’anic Ontology

Cited by 5 publications

References 4 publications

Development the Dataset for Automatic Translation System

Development the Dataset for Automatic Translation System

Recent Developments in Arabic Conversational AI: A Literature Review

AraConv: Developing an Arabic Task-Oriented Dialogue System Using Multi-Lingual Transformer Model mT5

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