A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and Instant Message

Elmadany, AbdelRahim A.; Abdou, Sherif M.; Gheith, Mervat

doi:10.5121/ijnlc.2015.4206

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…Furthermore, to date, few surveys have been done about Arabic chatbots to identify the techniques, metrics, and data sets used. However, chatbots that existed till 2018 are used to process the data in the survey of [15] and did not address some of the same research questions investigated in this SLR [16]. In addition, the survey in [17] highlights one approach to develop a chatbot instead of covering all of the three approaches that will be covered in this SLR.…”

Section: Introductionmentioning

confidence: 99%

English and Arabic Chatbots: A Systematic Literature Review

Alsheddi¹,

Alhenaki²

2022

IJACSA

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In recent years, the availability of chatbot applications has increased substantially with the advancement of artificial intelligence techniques, and research efforts have been active in the English language, which presents state-of-the-art solutions. However, despite the popularity of the Arabic language, its research community is still in an immature stage. Therefore, the main objective of this systematic literature review is studying state-of-the-art researchfor both the English and Arabic languagesto answer the proposed research questions regarding the development approaches, application domains, evaluation metrics, and development challenges of chatbot applications. The findings show that researchers have devoted more attention to the education domain using retrieval-based approaches while the generation-based approach has grown in popularity recently for providing new responses tasks. Whereas the hybrid approach for ranking multi-possible responses of combining both previous approaches shows a performance improvement. Besides, most metrics used to evaluate chatbot performance are human-based, followed by bilingual evaluation understudy and accuracy metrics. However, defining a common framework for evaluating chatbots remains a challenge. Finally, the open problems and future directions are highlighted to help in developing chatbots with minimal human interference to simulate natural conversations.

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Section: Introductionmentioning

confidence: 99%

English and Arabic Chatbots: A Systematic Literature Review

Alsheddi¹,

Alhenaki²

2022

IJACSA

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“…To the best of our knowledge, there is no published word for understanding Egyptian Arabic or Egyptian dialect. The survey by [27] presents background and the progress made in understanding Arabic dialogues.…”

Section: Related Workmentioning

confidence: 99%

Towards Understanding Egyptian Arabic Dialogues

Elmadany¹,

Abdou²,

Gheith³

2015

IJCA

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Labelling of user's utterances to understanding his attends which called Dialogue Act (DA) classification, it is considered the key player for dialogue language understanding layer in automatic dialogue systems. In this paper, we proposed a novel approach to user's utterances labeling for Egyptian spontaneous dialogues and Instant Messages using Machine Learning (ML) approach without relying on any special lexicons, cues, or rules. Due to the lack of Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus includes 4725 utterances for three domains, which are collected and annotated manually from Egyptian call-centers. The system achieves F 1 scores of 70.36% overall domains.

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