Building an Arabic Flight Booking Dialogue System Using a Hybrid Rule-Based and Data Driven Approach

Alajmi, A. F.; Al-Twairesh, Nora

doi:10.1109/access.2021.3049732

Cited by 18 publications

(14 citation statements)

References 20 publications

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“…For Arabic, some task-oriented DS have been built to serve specific domains, such as home automation [51], flight booking [52], and the medical domain [53]. Some are designed to serve specific Arabic dialects, such as OlloBot [53].…”

Section: Task-oriented Dialogue Systemsmentioning

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: Task-oriented Dialogue Systemsmentioning

confidence: 99%

“…Earlier, A. H. Al-Ajmi et al [52] proposed a text-based DS that is capable of handling customers' utterances in flight booking. They used a hybrid rule-based and data-driven approach in building their system.…”

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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“…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%

“…For Arabic user-based DS, several studies [37,38,41] have applied either pattern matching, rule-based, or rule-based and data-driven hybrid approaches to task-oriented DS. Nonetheless, it is apparent that most Arabic task-oriented DS use either rule-based or pattern matching approaches, with very few using a hybrid approach.…”

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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“…Conversation agents are either dialogue systems or chatbots. Dialogue systems perform specific functions such as making flight reservations [1]. Chatbots mimic human conversation [2].…”

Section: Introductionmentioning

confidence: 99%

Mining the Chatbot Brain to Improve COVID-19 Bot Response Accuracy

Ghaleb¹,

Almurtadha²,

Algarni³

et al. 2022

Computers, Materials &Amp; Continua

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People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses. However, chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope. Chatbots employ Natural Language Understanding (NLU) to infer their responses. There is a need for a chatbot that can learn from inquiries and expand its area of experience with time. This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval. This study proposes a methodology to enhance a chatbot's brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles. We used a COVID-19 information dataset to evaluate the proposed methodology. The pandemic has been accompanied by an "infodemic" of fake news. The chatbot was evaluated by a medical doctor and a public trial of 308 real users. Evaluations were obtained and statistically analyzed to measure effectiveness, efficiency, and satisfaction as described by the ISO9214 standard. The proposed COVID-19 chatbot system relieves doctors from answering questions. Chatbots provide an example of the use of technology to handle an infodemic.

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Building an Arabic Flight Booking Dialogue System Using a Hybrid Rule-Based and Data Driven Approach

Cited by 18 publications

References 20 publications

Recent Developments in Arabic Conversational AI: A Literature Review

Recent Developments in Arabic Conversational AI: A Literature Review

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

Mining the Chatbot Brain to Improve COVID-19 Bot Response Accuracy

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