International Conference on Information and Knowledge Technology

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Persian Language Understanding in Task-oriented Dialogue System for Online Shopping

Authors :

Zeinab Borhanifard¹ Hossein Basafa² Seyedeh Zahra Razavi³ Heshaam Faili⁴

1- دانشکده برق و کامپیوتر دانشگاه تهران 2- دانشکده برق و کامپیوتر دانشگاه تهران 3- University of Rochester 4- دانشکده برق و کامپیوتر دانشگاه تهران

Keywords :

task-oriented dialogue systems, natural language understanding, online shopping

Abstract :

Natural language understanding is a critical module in task-oriented dialogue systems. Recently, state-of-the-art approaches use deep learning methods and transformers to improve the performance of dialogue systems. In this work, we propose a natural language understanding model with a specific-shopping named entity recognizer using joint learning-based BERT transformer for task-oriented dialogue systems in the Persian Language. However, there is no published available dataset for Persian online shopping dialogue system, so to tackle the lack of data, we propose two methods for generating training data: fully-simulated and semi-simulated method. We created a simulated dataset with a hybrid of rule-based and template-based generation methods and a semi-simulated dataset where the language generation part is done by human to increase the quality of the dataset. Our experiments in the natural language understanding module show that a combination of the datasets can improve results. These dataset generation methods can apply in other domains for low-resource languages in task-oriented dialogue systems for solving the cold start problem of datasets.