Abstract
Approaches for developing Dialogue Systems (DSs) are typically categorized into rule-based and data-driven. Data-driven DSs require a massive quantity of training data, while rule-based DSs rely on a predefined set of rules and keywords that are to be detected in the user's utterances. The data-driven approaches show more promising results, but owing to a lack of available training data for Arabic task-oriented DSs, development of an Arabic task-oriented DS has typically been conducted using the rule-based approach. In this article, we propose a hybrid rule-based and data-driven approach in a text-based flight booking DS capable of handling customer's utterances. The proposed DS was built through utilizing the Wit.ai natural language interface. The conversation flow was configured using the Wizard of Oz technique, and the DS intents and entities were developed through the use of crowdsourcing training examples. The evaluation results show that the system developed was able to understand user utterances and to self-feed efficiently.