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English
صفحه اصلی
/
یازدهمین کنفرانس بین المللی فناوری اطلاعات و دانش
Persian Language Understanding in Task-oriented Dialogue System for Online Shopping
نویسندگان :
Zeinab Borhanifard
1
Hossein Basafa
2
Seyedeh Zahra Razavi
3
Heshaam Faili
4
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
کلمات کلیدی :
task-oriented dialogue systems, natural language understanding, online shopping
چکیده :
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.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 41.3.1