User Intent Detection in Persian Text-Based Chatbots: A Comprehensive Review of Methods and Challenges
Elham Amiri Abyaneh, Rahmat Zolfaghari, Alireza Amiri Abyaneh · 2025
User Intent Detection is one of the primary tasks in dialogue systems and chatbots, playing a key role in improving human-machine interaction. With the increasing use of chatbots in the Persian language, there is a growing need to develop efficient methods for detecting user intent in this language. This review article provides a comprehensive overview of the methods, challenges, and recent advancements in the field of user intent detection in Persian text-based chatbots. The review discusses deep learning-based methods, pre-trained language models, and language-specific challenges. Additionally, it analyzes existing datasets and data augmentation techniques to improve model performance. The findings of this review indicate that, despite recent advancements, challenges such as the scarcity of labeled data, structural differences between Persian and other languages, and the need for multilingual models persist. Finally, future research directions are proposed to enhance user intent detection in Persian chatbots.