Arabic Question Answering: A Study on Challenges, Systems, and Techniques

Samy, Heba; Hassanein, Ehab E.; Shaalan, Khaled

doi:10.5120/ijca2019918524

Cited by 5 publications

(10 citation statements)

References 11 publications

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“…Although deep learning approaches have revolutionized NLP for highresource languages such as English, Arabic is still in its infancy. This work significantly expands the scope of several existing surveys [13], [14], [15] by going beyond chatbots and question-answering systems to provide what we believe is the first general survey of text-based conversational AI systems in Arabic. This survey aims to shed light on recent studies focusing on Arabic conversational AI to provide an overview of the advancement of this field.…”

Section: Introductionmentioning

confidence: 88%

“…These challenges involve many linguistic factors, such as affixes, vowels, rootbased systems, diacritical marks, absence of capital letters, free word order, name de-spacing, inconsistent name spelling, dual forms, and grammatical gender variation. The literature provides more information on these challenges [13], [78].…”

Section: Morphological Issuesmentioning

confidence: 99%

“…For Arabic, surveys have tended to study the existing works of conversational agents until 2018, including QA systems and task-oriented DS [11], QA systems [12], [13], and chatbots [14], [15]. All these studies have mainly focused on the challenges of building these systems in Arabic.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 88%

Section: Morphological Issuesmentioning

confidence: 99%

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Recent Developments in Arabic Conversational AI: A Literature Review

Fuad

Al-Yahya

2022

IEEE Access

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“…In the first stage, the question is analyzed in terms of its syntax and semantics to extract the user’s intent, highlight the keywords, and generate the inquiry. This part of the question exposes its focus or primary purpose ( Samy, Hassanein & Shaalan, 2019 ). Furthermore, it determines the category of questions based on a predetermined classification and the expected response type.…”

Section: Introductionmentioning

confidence: 99%

“…NER is the first step toward answering factual inquiries ( Samy, Hassanein & Shaalan, 2019 ). During this step, the recognizer is responsible for extracting the names of persons and places.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive survey of techniques for developing an Arabic question answering system

Alkhurayyif

Sait

2023

PeerJ Computer Science

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The question-answering system (QAS) aims to produce a response to a query using information from a text corpus. Arabic is a complex language. However, it has more than 450 million native speakers across the globe. The Saudi Arabian government encourages organizations to automate their routine activities to provide adequate services to their stakeholders. The performance of current Arabic QASs is limited to the specific domain. An effective QAS retrieves relevant responses from structured and unstructured data based on the user query. Many QAS studies categorized QASs according to factors, including user queries, dataset characteristics, and the nature of the responses. A more comprehensive examination of QASs is required to improve the QAS development according to the present QAS requirements. The current literature presents the features and classifications of the Arabic QAS. There is a lack of studies to report the techniques of Arabic QAS development. Thus, this study suggests a systematic literature review of strategies for developing Arabic QAS. A total of 617 articles were collected, and 40 papers were included in the proposed review. The outcome reveals the importance of the dataset and the deep learning techniques used to improve the performance of the QAS. The existing systems depend on supervised learning methods that lower QAS performance. In addition, the recent development of machine learning techniques encourages researchers to develop unsupervised QAS.

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Deciphering Arabic question: a dedicated survey on Arabic question analysis methods, challenges, limitations and future pathways

Essam,

Deif,

Elgohary

2024

Artif Intell Rev

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This survey reviews different research on question analysis, including other comparative studies of question analysis approaches and an evaluation of the questions by different NLP techniques that are used in question interpretation and categorization. Among these key findings noted includes the assessment of deep learning models such as M-BiGRU-CNN and M-TF-IDF, which come with high precision and accuracy when applied with the effectiveness of use in dealing with the complexities involved in a language. Some of the most mature machine learning algorithms, for example, SVM or logistic regression, remain powerful models, especially on the classification task, meaning that the latter continues to be relevant. This study further underlines the applicability of rule-based or hybrid methodologies in certain linguistic situations, and it must be said that custom design solutions are required. We could recommend, on this basis, directing future work towards the integration of these hybrid systems and towards the definition of more general methodologies of evaluation that are in line with the constant evolution of NLP technologies. It revealed that the underlying challenges and barriers in the domain are very complex syntactic and dialectic variations, unavailability of software tools, very critical standardization in Arabic datasets, benchmark creation, handling of translated data, and the integration of Large Language Models (LLMs). The paper discusses the lack of identity and processing of such structures through online systems for comparison. This comprehensive review highlights not only the diversified potential for the capabilities of NLP techniques in refining question analysis but also the potential way of great promises for further enhancements and improvements in this progressive domain.

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Arabic Question Answering: A Study on Challenges, Systems, and Techniques

Cited by 5 publications

References 11 publications

Recent Developments in Arabic Conversational AI: A Literature Review

Recent Developments in Arabic Conversational AI: A Literature Review

A comprehensive survey of techniques for developing an Arabic question answering system

Deciphering Arabic question: a dedicated survey on Arabic question analysis methods, challenges, limitations and future pathways

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