ArabChat: An Arabic Conversational Agent

Hijjawi, Mohammad; Bandar, Zuhair; Crockett, Keeley; McLean, David

doi:10.1109/csit.2014.6806005

Cited by 28 publications

(75 citation statements)

References 8 publications

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“…ArabChat with classification methodology [19] is another ArabChat [17] update by Hijjawi, Bandar and Crockett. Using a new classification methodology for Arabic utterances.…”

Section: ) Aiml Approachmentioning

confidence: 99%

“…Enhanced ArabChat [18] is an updated version of ArabChat [17] by Hijjawi, Bandar and Crockett. This version uses extra features including Utterance Classification and Hybrid Rule.…”

Section: ) Aiml Approachmentioning

confidence: 99%

“…Mobile ArabChat [20] is based on the original ArabChat [17] and it is a mobile-based conversational agent and it is also used to work as an advisor for students in Applied Science University in Amman. It is a light version of ArabChat implemented in Android.…”

Section: ) Aiml Approachmentioning

confidence: 99%

“…Table I shows a summary on the discussed textual conversation chatbots that uses AIML approach. [17] implemented the ArabChat, which is a conversational agent web interface. The chatbot conversation domain is closed, designed to serve the students of the Applied Science University in Jordon.…”

Section: ) Aiml Approachmentioning

confidence: 99%

See 3 more Smart Citations

Arabic Chatbots: A Survey

Alhumoud¹,

Wazrah²,

Aldamegh³

2018

ijacsa

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Abstract-A Chatbot is a programmed entity that handles human-like conversations between an artificial agent and humans. This conversation has attracted the attention of researchers who are interested in the interaction between humans and machines to make the conversation more rational and hence pass the Turing test. The available research done in the field of Arabic chatbots is comparably scarce. This paper presents a review of the published Arabic chatbots studies to identify the gap of knowledge and to highlight the areas that needs more study and research. This study concluded the rarity of available research on Arabic chatbots and that all available works are retrieval based.

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“…ArabChat with classification methodology [19] is another ArabChat [17] update by Hijjawi, Bandar and Crockett. Using a new classification methodology for Arabic utterances.…”

Section: ) Aiml Approachmentioning

confidence: 99%

“…Enhanced ArabChat [18] is an updated version of ArabChat [17] by Hijjawi, Bandar and Crockett. This version uses extra features including Utterance Classification and Hybrid Rule.…”

Section: ) Aiml Approachmentioning

confidence: 99%

Section: ) Aiml Approachmentioning

confidence: 99%

Section: ) Aiml Approachmentioning

confidence: 99%

See 2 more Smart Citations

Arabic Chatbots: A Survey

Alhumoud¹,

Wazrah²,

Aldamegh³

2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…The game consider the first human fail if he/she failed to judge that the second party in the conversation was a human or a machine. From Turing's time, many of CAs has been proposed and developed until these days such as ELIZA [2], ALICE [3], ADAM [4], Museum Guide [5], InfoChat [6], InCA [7], Abdullah [8]andArabChat [9]. Most of the previous CAs was for English language.…”

Section: Introductionmentioning

confidence: 99%

Arabic Language Challenges in Text Based Conversational Agents Compared to The English Language

Hijjawi¹,

Elsheikh²

2015

IJCSIT

Self Cite

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DarijaBanking: A new resource for overcoming language barriers in banking intent detection for Moroccan Arabic speakers

Skiredj,

Azhari,

Berrada

et al. 2024

Nat. lang. processing

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Navigating the complexities of language diversity is a central challenge in developing robust natural language processing systems, especially in specialized domains like banking. The Moroccan Dialect of Arabic (Darija) serves as a common language that blends cultural complexities, historical impacts, and regional differences, which presents unique challenges for language models due to its divergence from Modern Standard Arabic and influence from French, Spanish, and Tamazight. To tackle these challenges, this paper introduces Darija Banking, a novel Darija dataset aimed at enhancing intent classification in the banking domain. DarijaBanking comprises over 1800 parallel high-quality queries in Darija, Modern Standard Arabic (MSA), English, and French, organized into 24 intent classes. We experimented various intent classification methods, including full fine-tuning of monolingual and multilingual models, zero-shot learning, retrieval-based approaches, and Large Language Model prompting. Furthermore, we propose BERTouch, a BERT-based language model fine-tuned on intent detection in Darija, which outperforms state-of-the-art models, including OpenAI’s GPT-4, achieving F1 scores of 0,98 and 0,96 on both Darija and MSA, respectively. The results provide insights into enhancing Moroccan Darija banking intent detection systems, highlighting the value of domain-specific data annotation and balancing precision and cost-effectiveness.

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ArabChat: An Arabic Conversational Agent

Cited by 28 publications

References 8 publications

Arabic Chatbots: A Survey

Arabic Chatbots: A Survey

Arabic Language Challenges in Text Based Conversational Agents Compared to The English Language

DarijaBanking: A new resource for overcoming language barriers in banking intent detection for Moroccan Arabic speakers

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